Datasets:

AlgorithmicResearchGroup

/

arxiv_deep_learning_python_research_code

like 6

3DG-STFM

3DG-STFM-master/src/loftr/backbone/resnet_fpn.py

import torch.nn as nn import torch.nn.functional as F def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution without padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): def __init__(self, in_planes, planes, stride=1): super().__init__() self.conv1 = conv3x3(in_planes, planes, stride) self.conv2 = conv3x3(planes, planes) self.bn1 = nn.BatchNorm2d(planes) self.bn2 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) if stride == 1: self.downsample = None else: self.downsample = nn.Sequential( conv1x1(in_planes, planes, stride=stride), nn.BatchNorm2d(planes) ) def forward(self, x): y = x y = self.relu(self.bn1(self.conv1(y))) y = self.bn2(self.conv2(y)) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) class ResNetFPN_8_2_RGB(nn.Module): """ ResNet+FPN, output resolution are 1/8 and 1/2. Each block has 2 layers. """ def __init__(self, config): super().__init__() # Config block = BasicBlock initial_dim = config['initial_dim'] block_dims = config['block_dims'] # Class Variable self.in_planes = initial_dim # Networks self.conv1 = nn.Conv2d(3, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(initial_dim) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 # 3. FPN upsample self.layer3_outconv = conv1x1(block_dims[2], block_dims[2]) self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) self.layer2_outconv2 = nn.Sequential( conv3x3(block_dims[2], block_dims[2]), nn.BatchNorm2d(block_dims[2]), nn.LeakyReLU(), conv3x3(block_dims[2], block_dims[1]), ) self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) self.layer1_outconv2 = nn.Sequential( conv3x3(block_dims[1], block_dims[1]), nn.BatchNorm2d(block_dims[1]), nn.LeakyReLU(), conv3x3(block_dims[1], block_dims[0]), ) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _make_layer(self, block, dim, stride=1): layer1 = block(self.in_planes, dim, stride=stride) layer2 = block(dim, dim, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # ResNet Backbone x0 = self.relu(self.bn1(self.conv1(x))) x1 = self.layer1(x0) # 1/2 x2 = self.layer2(x1) # 1/4 x3 = self.layer3(x2) # 1/8 # FPN x3_out = self.layer3_outconv(x3) x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) x2_out = self.layer2_outconv(x2) x2_out = self.layer2_outconv2(x2_out+x3_out_2x) x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True) x1_out = self.layer1_outconv(x1) x1_out = self.layer1_outconv2(x1_out+x2_out_2x) return [x3_out, x1_out] class ResNetFPN_8_2_RGBD(nn.Module): """ ResNet+FPN, output resolution are 1/8 and 1/2. Each block has 2 layers. """ def __init__(self, config): super().__init__() # Config block = BasicBlock initial_dim = config['initial_dim'] block_dims = config['block_dims'] # Class Variable self.in_planes = initial_dim # Networks self.conv1 = nn.Conv2d(4, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(initial_dim) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 # 3. FPN upsample self.layer3_outconv = conv1x1(block_dims[2], block_dims[2]) self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) self.layer2_outconv2 = nn.Sequential( conv3x3(block_dims[2], block_dims[2]), nn.BatchNorm2d(block_dims[2]), nn.LeakyReLU(), conv3x3(block_dims[2], block_dims[1]), ) self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) self.layer1_outconv2 = nn.Sequential( conv3x3(block_dims[1], block_dims[1]), nn.BatchNorm2d(block_dims[1]), nn.LeakyReLU(), conv3x3(block_dims[1], block_dims[0]), ) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) def _make_layer(self, block, dim, stride=1): layer1 = block(self.in_planes, dim, stride=stride) layer2 = block(dim, dim, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # ResNet Backbone x0 = self.relu(self.bn1(self.conv1(x))) x1 = self.layer1(x0) # 1/2 x2 = self.layer2(x1) # 1/4 x3 = self.layer3(x2) # 1/8 # FPN x3_out = self.layer3_outconv(x3) x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) x2_out = self.layer2_outconv(x2) x2_out = self.layer2_outconv2(x2_out+x3_out_2x) x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True) x1_out = self.layer1_outconv(x1) x1_out = self.layer1_outconv2(x1_out+x2_out_2x) return [x3_out, x1_out]

py

3DG-STFM

3DG-STFM-master/src/loftr/loftr_module/linear_attention.py

""" Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py """ import torch from torch.nn import Module, Dropout def elu_feature_map(x): return torch.nn.functional.elu(x) + 1 class LinearAttention(Module): def __init__(self, eps=1e-6): super().__init__() self.feature_map = elu_feature_map self.eps = eps def forward(self, queries, keys, values, q_mask=None, kv_mask=None): """ Multi-Head linear attention proposed in "Transformers are RNNs" Args: queries: [N, L, H, D] keys: [N, S, H, D] values: [N, S, H, D] q_mask: [N, L] kv_mask: [N, S] Returns: queried_values: (N, L, H, D) """ Q = self.feature_map(queries) K = self.feature_map(keys) # set padded position to zero if q_mask is not None: Q = Q * q_mask[:, :, None, None] if kv_mask is not None: K = K * kv_mask[:, :, None, None] values = values * kv_mask[:, :, None, None] v_length = values.size(1) values = values / v_length # prevent fp16 overflow KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps) queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length return queried_values.contiguous() class FullAttention(Module): def __init__(self, use_dropout=False, attention_dropout=0.1): super().__init__() self.use_dropout = use_dropout self.dropout = Dropout(attention_dropout) def forward(self, queries, keys, values, q_mask=None, kv_mask=None): """ Multi-head scaled dot-product attention, a.k.a full attention. Args: queries: [N, L, H, D] keys: [N, S, H, D] values: [N, S, H, D] q_mask: [N, L] kv_mask: [N, S] Returns: queried_values: (N, L, H, D) """ # Compute the unnormalized attention and apply the masks QK = torch.einsum("nlhd,nshd->nlsh", queries, keys) if kv_mask is not None: QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float('-inf')) # Compute the attention and the weighted average softmax_temp = 1. / queries.size(3)**.5 # sqrt(D) A = torch.softmax(softmax_temp * QK, dim=2) if self.use_dropout: A = self.dropout(A) queried_values = torch.einsum("nlsh,nshd->nlhd", A, values) return queried_values.contiguous()

py

3DG-STFM

3DG-STFM-master/src/loftr/loftr_module/fine_preprocess.py

import torch import torch.nn as nn import torch.nn.functional as F from einops.einops import rearrange, repeat class FinePreprocess(nn.Module): def __init__(self, config): super().__init__() self.config = config self.cat_c_feat = config['fine_concat_coarse_feat'] self.W = self.config['fine_window_size'] d_model_c = self.config['coarse']['d_model'] d_model_f = self.config['fine']['d_model'] self.d_model_f = d_model_f if self.cat_c_feat: self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True) self.merge_feat = nn.Linear(2*d_model_f, d_model_f, bias=True) self._reset_parameters() def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu") def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data): W = self.W stride = data['hw0_f'][0] // data['hw0_c'][0] data.update({'W': W}) if data['b_ids'].shape[0] == 0: feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) return feat0, feat1 # 1. unfold(crop) all local windows feat_f0_unfold = F.unfold(feat_f0, kernel_size=(W, W), stride=stride, padding=W//2) feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2) feat_f1_unfold = F.unfold(feat_f1, kernel_size=(W, W), stride=stride, padding=W//2) feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2) # 2. select only the predicted matches feat_f0_unfold = feat_f0_unfold[data['b_ids'], data['i_ids']] # [n, ww, cf] feat_f1_unfold = feat_f1_unfold[data['b_ids'], data['j_ids']] # option: use coarse-level loftr feature as context: concat and linear if self.cat_c_feat: feat_c_win = self.down_proj(torch.cat([feat_c0[data['b_ids'], data['i_ids']], feat_c1[data['b_ids'], data['j_ids']]], 0)) # [2n, c] feat_cf_win = self.merge_feat(torch.cat([ torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf] repeat(feat_c_win, 'n c -> n ww c', ww=W**2), # [2n, ww, cf] ], -1)) feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0) return feat_f0_unfold, feat_f1_unfold class FinePreprocess_t(nn.Module): def __init__(self, config): super().__init__() self.config = config self.cat_c_feat = config['fine_concat_coarse_feat'] self.W = self.config['fine_window_size'] d_model_c = self.config['coarse']['d_model'] d_model_f = self.config['fine']['d_model'] self.d_model_f = d_model_f if self.cat_c_feat: self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True) self.merge_feat = nn.Linear(2*d_model_f, d_model_f, bias=True) self._reset_parameters() def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu") def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data): W = self.W stride = data['hw0_f'][0] // data['hw0_c'][0] data.update({'W': W}) if data['b_ids_t'].shape[0] == 0: feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) return feat0, feat1 # 1. unfold(crop) all local windows feat_f0_unfold = F.unfold(feat_f0, kernel_size=(W, W), stride=stride, padding=W//2) feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2) feat_f1_unfold = F.unfold(feat_f1, kernel_size=(W, W), stride=stride, padding=W//2) feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2) # 2. select only the predicted matches feat_f0_unfold = feat_f0_unfold[data['b_ids_t'], data['i_ids_t']] # [n, ww, cf] feat_f1_unfold = feat_f1_unfold[data['b_ids_t'], data['j_ids_t']] # option: use coarse-level loftr feature as context: concat and linear if self.cat_c_feat: feat_c_win = self.down_proj(torch.cat([feat_c0[data['b_ids_t'], data['i_ids_t']], feat_c1[data['b_ids_t'], data['j_ids_t']]], 0)) # [2n, c] feat_cf_win = self.merge_feat(torch.cat([ torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf] repeat(feat_c_win, 'n c -> n ww c', ww=W**2), # [2n, ww, cf] ], -1)) feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0) return feat_f0_unfold, feat_f1_unfold

py

3DG-STFM

3DG-STFM-master/src/loftr/loftr_module/transformer.py

import copy import torch import torch.nn as nn from .linear_attention import LinearAttention, FullAttention class LoFTREncoderLayer(nn.Module): def __init__(self, d_model, nhead, attention='linear'): super(LoFTREncoderLayer, self).__init__() self.dim = d_model // nhead self.nhead = nhead # multi-head attention self.q_proj = nn.Linear(d_model, d_model, bias=False) self.k_proj = nn.Linear(d_model, d_model, bias=False) self.v_proj = nn.Linear(d_model, d_model, bias=False) self.attention = LinearAttention() if attention == 'linear' else FullAttention() self.merge = nn.Linear(d_model, d_model, bias=False) # feed-forward network self.mlp = nn.Sequential( nn.Linear(d_model*2, d_model*2, bias=False), nn.ReLU(True), nn.Linear(d_model*2, d_model, bias=False), ) # norm and dropout self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) def forward(self, x, source, x_mask=None, source_mask=None): """ Args: x (torch.Tensor): [N, L, C] source (torch.Tensor): [N, S, C] x_mask (torch.Tensor): [N, L] (optional) source_mask (torch.Tensor): [N, S] (optional) """ bs = x.size(0) query, key, value = x, source, source # multi-head attention query = self.q_proj(query).view(bs, -1, self.nhead, self.dim) # [N, L, (H, D)] key = self.k_proj(key).view(bs, -1, self.nhead, self.dim) # [N, S, (H, D)] value = self.v_proj(value).view(bs, -1, self.nhead, self.dim) message = self.attention(query, key, value, q_mask=x_mask, kv_mask=source_mask) # [N, L, (H, D)] message = self.merge(message.view(bs, -1, self.nhead*self.dim)) # [N, L, C] message = self.norm1(message) # feed-forward network message = self.mlp(torch.cat([x, message], dim=2)) message = self.norm2(message) return x + message class LocalFeatureTransformer(nn.Module): """A Local Feature Transformer (LoFTR) module.""" def __init__(self, config): super(LocalFeatureTransformer, self).__init__() self.config = config self.d_model = config['d_model'] self.nhead = config['nhead'] self.layer_names = config['layer_names'] encoder_layer = LoFTREncoderLayer(config['d_model'], config['nhead'], config['attention']) self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]) self._reset_parameters() def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p) def forward(self, feat0, feat1, mask0=None, mask1=None): """ Args: feat0 (torch.Tensor): [N, L, C] feat1 (torch.Tensor): [N, S, C] mask0 (torch.Tensor): [N, L] (optional) mask1 (torch.Tensor): [N, S] (optional) """ assert self.d_model == feat0.size(2), "the feature number of src and transformer must be equal" for layer, name in zip(self.layers, self.layer_names): if name == 'self': feat0 = layer(feat0, feat0, mask0, mask0) feat1 = layer(feat1, feat1, mask1, mask1) elif name == 'cross': feat0 = layer(feat0, feat1, mask0, mask1) feat1 = layer(feat1, feat0, mask1, mask0) else: raise KeyError return feat0, feat1

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/supervision.py

from math import log from loguru import logger import torch from einops import repeat from kornia.utils import create_meshgrid from .geometry import warp_kpts ############## ↓ Coarse-Level supervision ↓ ############## @torch.no_grad() def mask_pts_at_padded_regions(grid_pt, mask): """For megadepth dataset, zero-padding exists in images""" mask = repeat(mask, 'n h w -> n (h w) c', c=2) grid_pt[~mask.bool()] = 0 return grid_pt @torch.no_grad() def spvs_coarse(data, config): """ Update: data (dict): { "conf_matrix_gt": [N, hw0, hw1], 'spv_b_ids': [M] 'spv_i_ids': [M] 'spv_j_ids': [M] 'spv_w_pt0_i': [N, hw0, 2], in original image resolution 'spv_pt1_i': [N, hw1, 2], in original image resolution } NOTE: - for scannet dataset, there're 3 kinds of resolution {i, c, f} - for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f} """ # 1. misc device = data['image0'].device N, _, H0, W0 = data['image0'].shape _, _, H1, W1 = data['image1'].shape scale = config['LOFTR']['RESOLUTION'][0] scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1]) # 2. warp grids # create kpts in meshgrid and resize them to image resolution grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2] grid_pt0_i = scale0 * grid_pt0_c grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1) grid_pt1_i = scale1 * grid_pt1_c # mask padded region to (0, 0), so no need to manually mask conf_matrix_gt if 'mask0' in data: grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0']) grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1']) # warp kpts bi-directionally and resize them to coarse-level resolution # (no depth consistency check, since it leads to worse results experimentally) # (unhandled edge case: points with 0-depth will be warped to the left-up corner) _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1']) _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0']) w_pt0_c = w_pt0_i / scale1 w_pt1_c = w_pt1_i / scale0 # 3. check if mutual nearest neighbor w_pt0_c_round = w_pt0_c[:, :, :].round().long() nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1 w_pt1_c_round = w_pt1_c[:, :, :].round().long() nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0 # corner case: out of boundary def out_bound_mask(pt, w, h): return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h) nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0 nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0 loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0) correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1) correct_0to1[:, 0] = False # ignore the top-left corner # 4. construct a gt conf_matrix conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device) b_ids, i_ids = torch.where(correct_0to1 != 0) j_ids = nearest_index1[b_ids, i_ids] conf_matrix_gt[b_ids, i_ids, j_ids] = 1 data.update({'conf_matrix_gt': conf_matrix_gt}) # 5. save coarse matches(gt) for training fine level if len(b_ids) == 0: logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}") # this won't affect fine-level loss calculation b_ids = torch.tensor([0], device=device) i_ids = torch.tensor([0], device=device) j_ids = torch.tensor([0], device=device) data.update({ 'spv_b_ids': b_ids, 'spv_i_ids': i_ids, 'spv_j_ids': j_ids }) # 6. save intermediate results (for fast fine-level computation) data.update({ 'spv_w_pt0_i': w_pt0_i, 'spv_pt1_i': grid_pt1_i }) def compute_supervision_coarse(data, config): assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!" data_source = data['dataset_name'][0] if data_source.lower() in ['scannet', 'megadepth']: spvs_coarse(data, config) else: raise ValueError(f'Unknown data source: {data_source}') ############## ↓ Fine-Level supervision ↓ ############## @torch.no_grad() def spvs_fine(data, config): """ Update: data (dict):{ "expec_f_gt": [M, 2]} """ # 1. misc # w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i') w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i'] scale = config['LOFTR']['RESOLUTION'][1] radius = config['LOFTR']['FINE_WINDOW_SIZE'] // 2 # 2. get coarse prediction b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids'] # 3. compute gt scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale # `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2] data.update({"expec_f_gt": expec_f_gt}) def compute_supervision_fine(data, config): data_source = data['dataset_name'][0] if data_source.lower() in ['scannet', 'megadepth']: spvs_fine(data, config) else: raise NotImplementedError

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/position_encoding.py

import math import torch from torch import nn class PositionEncodingSine(nn.Module): """ This is a sinusoidal position encoding that generalized to 2-dimensional images """ def __init__(self, d_model, max_shape=(256, 256)): """ Args: max_shape (tuple): for 1/8 featmap, the max length of 256 corresponds to 2048 pixels """ super().__init__() pe = torch.zeros((d_model, *max_shape)) y_position = torch.ones(max_shape).cumsum(0).float().unsqueeze(0) x_position = torch.ones(max_shape).cumsum(1).float().unsqueeze(0) div_term = torch.exp(torch.arange(0, d_model//2, 2).float() * (-math.log(10000.0) / d_model//2)) div_term = div_term[:, None, None] # [C//4, 1, 1] pe[0::4, :, :] = torch.sin(x_position * div_term) pe[1::4, :, :] = torch.cos(x_position * div_term) pe[2::4, :, :] = torch.sin(y_position * div_term) pe[3::4, :, :] = torch.cos(y_position * div_term) self.register_buffer('pe', pe.unsqueeze(0), persistent=False) # [1, C, H, W] def forward(self, x): """ Args: x: [N, C, H, W] """ return x + self.pe[:, :, :x.size(2), :x.size(3)]

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/fine_matching.py

import math import torch import torch.nn as nn from kornia.geometry.subpix import dsnt from kornia.utils.grid import create_meshgrid class FineMatching(nn.Module): """FineMatching with s2d paradigm""" def __init__(self): super().__init__() def forward(self, feat_f0, feat_f1, data): """ Args: feat0 (torch.Tensor): [M, WW, C] feat1 (torch.Tensor): [M, WW, C] data (dict) Update: data (dict):{ 'expec_f' (torch.Tensor): [M, 3], 'mkpts0_f' (torch.Tensor): [M, 2], 'mkpts1_f' (torch.Tensor): [M, 2]} """ M, WW, C = feat_f0.shape W = int(math.sqrt(WW)) scale = data['hw0_i'][0] / data['hw0_f'][0] self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale # corner case: if no coarse matches found if M == 0: assert self.training == False, "M is always >0, when training, see coarse_matching.py" # logger.warning('No matches found in coarse-level.') data.update({ 'expec_f': torch.empty(0, 3, device=feat_f0.device), 'mkpts0_f': data['mkpts0_c'], 'mkpts1_f': data['mkpts1_c'], }) return feat_f0_picked = feat_f0_picked = feat_f0[:, WW//2, :] sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1) softmax_temp = 1. / C**.5 heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1).view(-1, W, W) # compute coordinates from heatmap coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2] grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2] # compute std over <x, y> var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords_normalized**2 # [M, 2] std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability # for fine-level supervision data.update({'expec_f': torch.cat([coords_normalized, std.unsqueeze(1)], -1)}) # compute absolute kpt coords self.get_fine_match(coords_normalized, data) @torch.no_grad() def get_fine_match(self, coords_normed, data): W, WW, C, scale = self.W, self.WW, self.C, self.scale # mkpts0_f and mkpts1_f mkpts0_f = data['mkpts0_c'] scale1 = scale * data['scale1'][data['b_ids']] if 'scale0' in data else scale mkpts1_f = data['mkpts1_c'] + (coords_normed * (W // 2) * scale1)[:len(data['mconf'])] data.update({ "mkpts0_f": mkpts0_f, "mkpts1_f": mkpts1_f }) class FineMatching_t(nn.Module): """FineMatching with s2d paradigm""" def __init__(self): super().__init__() def forward(self, feat_f0, feat_f1, data): """ Args: feat0 (torch.Tensor): [M, WW, C] feat1 (torch.Tensor): [M, WW, C] data (dict) Update: data (dict):{ 'expec_f' (torch.Tensor): [M, 3], 'mkpts0_f' (torch.Tensor): [M, 2], 'mkpts1_f' (torch.Tensor): [M, 2]} """ M, WW, C = feat_f0.shape W = int(math.sqrt(WW)) scale = data['hw0_i'][0] / data['hw0_f'][0] self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale # corner case: if no coarse matches found if M == 0: assert self.training == False, "M is always >0, when training, see coarse_matching.py" # logger.warning('No matches found in coarse-level.') data.update({ 'expec_f_t': torch.empty(0, 3, device=feat_f0.device), 'mkpts0_f_t': data['mkpts0_c'], 'mkpts1_f_T': data['mkpts1_c'], }) return feat_f0_picked = feat_f0_picked = feat_f0[:, WW // 2, :] sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1) softmax_temp = 1. / C ** .5 heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1).view(-1, W, W) # compute coordinates from heatmap coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2] grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2] # compute std over <x, y> var = torch.sum(grid_normalized ** 2 * heatmap.view(-1, WW, 1), dim=1) - coords_normalized ** 2 # [M, 2] std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability # for fine-level supervision data.update({'expec_f_t': torch.cat([coords_normalized, std.unsqueeze(1)], -1)}) # compute absolute kpt coords self.get_fine_match(coords_normalized, data) @torch.no_grad() def get_fine_match(self, coords_normed, data): W, WW, C, scale = self.W, self.WW, self.C, self.scale # mkpts0_f and mkpts1_f mkpts0_f = data['mkpts0_c'] scale1 = scale * data['scale1'][data['b_ids']] if 'scale0' in data else scale mkpts1_f = data['mkpts1_c'] + (coords_normed * (W // 2) * scale1)[:len(data['mconf'])] data.update({ "mkpts0_f": mkpts0_f, "mkpts1_f": mkpts1_f })

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/supervision_homography.py

from math import log from loguru import logger import torch from einops import repeat from kornia.utils import create_meshgrid from .geometry import warp_kpts,warp_kpts_homo ############## ↓ Coarse-Level supervision ↓ ############## @torch.no_grad() def mask_pts_at_padded_regions(grid_pt, mask): """For megadepth dataset, zero-padding exists in images""" mask = repeat(mask, 'n h w -> n (h w) c', c=2) grid_pt[~mask.bool()] = 0 return grid_pt @torch.no_grad() def spvs_coarse(data, config): """ Update: data (dict): { "conf_matrix_gt": [N, hw0, hw1], 'spv_b_ids': [M] 'spv_i_ids': [M] 'spv_j_ids': [M] 'spv_w_pt0_i': [N, hw0, 2], in original image resolution 'spv_pt1_i': [N, hw1, 2], in original image resolution } NOTE: - for scannet dataset, there're 3 kinds of resolution {i, c, f} - for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f} """ # 1. misc device = data['image0'].device N, _, H0, W0 = data['image0'].shape _, _, H1, W1 = data['image1'].shape scale = config['LOFTR']['RESOLUTION'][0] scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1]) # 2. warp grids # create kpts in meshgrid and resize them to image resolution grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0 * w0, 2).repeat(N, 1, 1) # [N, hw, 2] grid_pt0_i = scale0 * grid_pt0_c grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1 * w1, 2).repeat(N, 1, 1) grid_pt1_i = scale1 * grid_pt1_c # mask padded region to (0, 0), so no need to manually mask conf_matrix_gt if 'mask0' in data: grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0']) grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1']) # warp kpts bi-directionally and resize them to coarse-level resolution # (no depth consistency check, since it leads to worse results experimentally) # (unhandled edge case: points with 0-depth will be warped to the left-up corner) w_pt0_i = warp_kpts_homo(grid_pt0_i, data['M']) inv_M = torch.inverse(data['M']) w_pt1_i = warp_kpts_homo(grid_pt1_i, inv_M) #_, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1']) #_, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0']) w_pt0_c = w_pt0_i / scale1 w_pt1_c = w_pt1_i / scale0 # 3. check if mutual nearest neighbor w_pt0_c_round = w_pt0_c[:, :, :].round().long() nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1 w_pt1_c_round = w_pt1_c[:, :, :].round().long() nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0 # corner case: out of boundary def out_bound_mask(pt, w, h): return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h) nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0 nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0 loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0) correct_0to1 = loop_back == torch.arange(h0 * w0, device=device)[None].repeat(N, 1) correct_0to1[:, 0] = False # ignore the top-left corner # 4. construct a gt conf_matrix conf_matrix_gt = torch.zeros(N, h0 * w0, h1 * w1, device=device) b_ids, i_ids = torch.where(correct_0to1 != 0) j_ids = nearest_index1[b_ids, i_ids] conf_matrix_gt[b_ids, i_ids, j_ids] = 1 data.update({'conf_matrix_gt': conf_matrix_gt}) # 5. save coarse matches(gt) for training fine level if len(b_ids) == 0: logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}") # this won't affect fine-level loss calculation b_ids = torch.tensor([0], device=device) i_ids = torch.tensor([0], device=device) j_ids = torch.tensor([0], device=device) data.update({ 'spv_b_ids': b_ids, 'spv_i_ids': i_ids, 'spv_j_ids': j_ids }) # 6. save intermediate results (for fast fine-level computation) data.update({ 'spv_w_pt0_i': w_pt0_i, 'spv_pt1_i': grid_pt1_i }) def compute_supervision_coarse_homo(data, config): spvs_coarse(data, config) #assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!" #data_source = data['dataset_name'][0] #if data_source.lower() in ['scannet', 'megadepth']: # spvs_coarse(data, config) #else: # raise ValueError(f'Unknown data source: {data_source}') ############## ↓ Fine-Level supervision ↓ ############## @torch.no_grad() def spvs_fine(data, config): """ Update: data (dict):{ "expec_f_gt": [M, 2]} """ # 1. misc # w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i') w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i'] scale = config['LOFTR']['RESOLUTION'][1] radius = config['LOFTR']['FINE_WINDOW_SIZE'] // 2 # 2. get coarse prediction b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids'] # 3. compute gt scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale # `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2] data.update({"expec_f_gt": expec_f_gt}) def compute_supervision_fine_homo(data, config): spvs_fine(data, config) #data_source = data['dataset_name'][0] #if data_source.lower() in ['scannet', 'megadepth']: # spvs_fine(data, config) #else: # raise NotImplementedError

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/geometry.py

import torch import cv2 @torch.no_grad() def warp_kpts_homo(kpts0, M): """ Warp kpts0 from I0 to I1 with Homography M Args: kpts0 (torch.Tensor): [N, L, 2] - <x, y>, M (torch.Tensor): Returns: warped_keypoints0 (torch.Tensor): [N, L, 2] <x0_hat, y1_hat> """ #kpts0_long = kpts0.round().long() #print(kpts0_long.cpu().numpy()[None].shape) #print(M.cpu().numpy().shape) #print(kpts0.size()) #kpts1 = cv2.perspectiveTransform(kpts0_long.cpu().numpy()[None], M.cpu().numpy()) #project device = kpts0.device w_kpts0 = cv2.perspectiveTransform(kpts0.cpu().numpy(), M.cpu().numpy()[0]) w_kpts0 = torch.from_numpy(w_kpts0) w_kpts0 = w_kpts0.to(device) #print(device,M) return w_kpts0 @torch.no_grad() def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1): """ Warp kpts0 from I0 to I1 with depth, K and Rt Also check covisibility and depth consistency. Depth is consistent if relative error < 0.2 (hard-coded). Args: kpts0 (torch.Tensor): [N, L, 2] - <x, y>, depth0 (torch.Tensor): [N, H, W], depth1 (torch.Tensor): [N, H, W], T_0to1 (torch.Tensor): [N, 3, 4], K0 (torch.Tensor): [N, 3, 3], K1 (torch.Tensor): [N, 3, 3], Returns: calculable_mask (torch.Tensor): [N, L] warped_keypoints0 (torch.Tensor): [N, L, 2] <x0_hat, y1_hat> """ kpts0_long = kpts0.round().long() # Sample depth, get calculable_mask on depth != 0 kpts0_depth = torch.stack( [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0 ) # (N, L) nonzero_mask = kpts0_depth != 0 # Unproject kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3) kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) # Rigid Transform w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L) w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] # Project w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth # Covisible Check h, w = depth1.shape[1:3] covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \ (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1) w_kpts0_long = w_kpts0.long() w_kpts0_long[~covisible_mask, :] = 0 w_kpts0_depth = torch.stack( [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0 ) # (N, L) consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 valid_mask = nonzero_mask * covisible_mask * consistent_mask return valid_mask, w_kpts0

py

3DG-STFM

3DG-STFM-master/src/loftr/utils/coarse_matching.py

import torch import torch.nn as nn import torch.nn.functional as F from einops.einops import rearrange INF = 1e9 def mask_border(m, b: int, v): """ Mask borders with value Args: m (torch.Tensor): [N, H0, W0, H1, W1] b (int) v (m.dtype) """ if b <= 0: return m[:, :b] = v m[:, :, :b] = v m[:, :, :, :b] = v m[:, :, :, :, :b] = v m[:, -b:] = v m[:, :, -b:] = v m[:, :, :, -b:] = v m[:, :, :, :, -b:] = v def mask_border_with_padding(m, bd, v, p_m0, p_m1): if bd <= 0: return m[:, :bd] = v m[:, :, :bd] = v m[:, :, :, :bd] = v m[:, :, :, :, :bd] = v h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int() h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int() for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)): m[b_idx, h0 - bd:] = v m[b_idx, :, w0 - bd:] = v m[b_idx, :, :, h1 - bd:] = v m[b_idx, :, :, :, w1 - bd:] = v def compute_max_candidates(p_m0, p_m1): """Compute the max candidates of all pairs within a batch Args: p_m0, p_m1 (torch.Tensor): padded masks """ h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0] h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0] max_cand = torch.sum( torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0]) return max_cand class CoarseMatching(nn.Module): def __init__(self, config): super().__init__() self.config = config # general config self.thr = config['thr'] self.border_rm = config['border_rm'] # -- # for trainig fine-level LoFTR self.train_coarse_percent = config['train_coarse_percent'] self.train_pad_num_gt_min = config['train_pad_num_gt_min'] # we provide 2 options for differentiable matching self.match_type = config['match_type'] if self.match_type == 'dual_softmax': self.temperature = config['dsmax_temperature'] elif self.match_type == 'sinkhorn': try: from .superglue import log_optimal_transport except ImportError: raise ImportError("download superglue.py first!") self.log_optimal_transport = log_optimal_transport self.bin_score = nn.Parameter( torch.tensor(config['skh_init_bin_score'], requires_grad=True)) self.skh_iters = config['skh_iters'] self.skh_prefilter = config['skh_prefilter'] else: raise NotImplementedError() def forward(self, feat_c0, feat_c1, data, mask_c0=None, mask_c1=None): """ Args: feat0 (torch.Tensor): [N, L, C] feat1 (torch.Tensor): [N, S, C] data (dict) mask_c0 (torch.Tensor): [N, L] (optional) mask_c1 (torch.Tensor): [N, S] (optional) Update: data (dict): { 'b_ids' (torch.Tensor): [M'], 'i_ids' (torch.Tensor): [M'], 'j_ids' (torch.Tensor): [M'], 'gt_mask' (torch.Tensor): [M'], 'mkpts0_c' (torch.Tensor): [M, 2], 'mkpts1_c' (torch.Tensor): [M, 2], 'mconf' (torch.Tensor): [M]} NOTE: M' != M during training. """ N, L, S, C = feat_c0.size(0), feat_c0.size(1), feat_c1.size(1), feat_c0.size(2) # normalize feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5, [feat_c0, feat_c1]) if self.match_type == 'dual_softmax': sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) / self.temperature if mask_c0 is not None: sim_matrix.masked_fill_( ~(mask_c0[..., None] * mask_c1[:, None]).bool(), -INF) data.update({'sim_matrix': sim_matrix}) conf_matrix = F.softmax(sim_matrix, 1) * F.softmax(sim_matrix, 2) elif self.match_type == 'sinkhorn': # sinkhorn, dustbin included sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) if mask_c0 is not None: sim_matrix[:, :L, :S].masked_fill_( ~(mask_c0[..., None] * mask_c1[:, None]).bool(), -INF) # build uniform prior & use sinkhorn log_assign_matrix = self.log_optimal_transport( sim_matrix, self.bin_score, self.skh_iters) assign_matrix = log_assign_matrix.exp() conf_matrix = assign_matrix[:, :-1, :-1] # filter prediction with dustbin score (only in evaluation mode) if not self.training and self.skh_prefilter: filter0 = (assign_matrix.max(dim=2)[1] == S)[:, :-1] # [N, L] filter1 = (assign_matrix.max(dim=1)[1] == L)[:, :-1] # [N, S] conf_matrix[filter0[..., None].repeat(1, 1, S)] = 0 conf_matrix[filter1[:, None].repeat(1, L, 1)] = 0 if self.config['sparse_spvs']: data.update({'conf_matrix_with_bin': assign_matrix.clone()}) data.update({'conf_matrix': conf_matrix}) # predict coarse matches from conf_matrix data.update(**self.get_coarse_match(conf_matrix, data)) @torch.no_grad() def get_coarse_match(self, conf_matrix, data): """ Args: conf_matrix (torch.Tensor): [N, L, S] data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c'] Returns: coarse_matches (dict): { 'b_ids' (torch.Tensor): [M'], 'i_ids' (torch.Tensor): [M'], 'j_ids' (torch.Tensor): [M'], 'gt_mask' (torch.Tensor): [M'], 'm_bids' (torch.Tensor): [M], 'mkpts0_c' (torch.Tensor): [M, 2], 'mkpts1_c' (torch.Tensor): [M, 2], 'mconf' (torch.Tensor): [M]} """ axes_lengths = { 'h0c': data['hw0_c'][0], 'w0c': data['hw0_c'][1], 'h1c': data['hw1_c'][0], 'w1c': data['hw1_c'][1] } _device = conf_matrix.device # 1. confidence thresholding mask = conf_matrix > self.thr mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', **axes_lengths) if 'mask0' not in data: mask_border(mask, self.border_rm, False) else: mask_border_with_padding(mask, self.border_rm, False, data['mask0'], data['mask1']) mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', **axes_lengths) # 2. mutual nearest mask = mask \ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) # 3. find all valid coarse matches # this only works when at most one `True` in each row mask_v, all_j_ids = mask.max(dim=2) b_ids, i_ids = torch.where(mask_v) j_ids = all_j_ids[b_ids, i_ids] mconf = conf_matrix[b_ids, i_ids, j_ids] # 4. Random sampling of training samples for fine-level LoFTR # (optional) pad samples with gt coarse-level matches if self.training: # NOTE: # The sampling is performed across all pairs in a batch without manually balancing # #samples for fine-level increases w.r.t. batch_size if 'mask0' not in data: num_candidates_max = mask.size(0) * max( mask.size(1), mask.size(2)) else: num_candidates_max = compute_max_candidates( data['mask0'], data['mask1']) num_matches_train = int(num_candidates_max * self.train_coarse_percent) num_matches_pred = len(b_ids) assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches" # pred_indices is to select from prediction if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min: pred_indices = torch.arange(num_matches_pred, device=_device) else: pred_indices = torch.randint( num_matches_pred, (num_matches_train - self.train_pad_num_gt_min, ), device=_device) # gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200) gt_pad_indices = torch.randint( len(data['spv_b_ids']), (max(num_matches_train - num_matches_pred, self.train_pad_num_gt_min), ), device=_device) mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero #print(len(data['spv_b_ids']),b_ids.size(),mconf.size(),gt_pad_indices.size(),pred_indices.size()) #print([j_ids, data['spv_j_ids']]) #print(j_ids.size(), data['spv_j_ids'].size()) b_ids, i_ids, j_ids, mconf = map( lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], dim=0), *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']], [j_ids, data['spv_j_ids']], [mconf, mconf_gt])) #print(b_ids.size(),'---------here') # These matches select patches that feed into fine-level network coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids} # 4. Update with matches in original image resolution scale = data['hw0_i'][0] / data['hw0_c'][0] scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale mkpts0_c = torch.stack( [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], dim=1) * scale0 mkpts1_c = torch.stack( [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], dim=1) * scale1 # These matches is the current prediction (for visualization) coarse_matches.update({ 'gt_mask': mconf == 0, 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches 'mkpts0_c': mkpts0_c[mconf != 0], 'mkpts1_c': mkpts1_c[mconf != 0], 'mconf': mconf[mconf != 0] }) return coarse_matches class CoarseMatching_t(nn.Module): def __init__(self, config): super().__init__() self.config = config # general config self.thr = config['thr'] self.border_rm = config['border_rm'] # -- # for trainig fine-level LoFTR self.train_coarse_percent = config['train_coarse_percent'] self.train_pad_num_gt_min = config['train_pad_num_gt_min'] # we provide 2 options for differentiable matching self.match_type = config['match_type'] if self.match_type == 'dual_softmax': self.temperature = config['dsmax_temperature'] elif self.match_type == 'sinkhorn': try: from .superglue import log_optimal_transport except ImportError: raise ImportError("download superglue.py first!") self.log_optimal_transport = log_optimal_transport self.bin_score = nn.Parameter( torch.tensor(config['skh_init_bin_score'], requires_grad=True)) self.skh_iters = config['skh_iters'] self.skh_prefilter = config['skh_prefilter'] else: raise NotImplementedError() def forward(self, feat_c0, feat_c1, data, mask_c0=None, mask_c1=None): """ Args: feat0 (torch.Tensor): [N, L, C] feat1 (torch.Tensor): [N, S, C] data (dict) mask_c0 (torch.Tensor): [N, L] (optional) mask_c1 (torch.Tensor): [N, S] (optional) Update: data (dict): { 'b_ids' (torch.Tensor): [M'], 'i_ids' (torch.Tensor): [M'], 'j_ids' (torch.Tensor): [M'], 'gt_mask' (torch.Tensor): [M'], 'mkpts0_c' (torch.Tensor): [M, 2], 'mkpts1_c' (torch.Tensor): [M, 2], 'mconf' (torch.Tensor): [M]} NOTE: M' != M during training. """ N, L, S, C = feat_c0.size(0), feat_c0.size(1), feat_c1.size(1), feat_c0.size(2) # normalize feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5, [feat_c0, feat_c1]) if self.match_type == 'dual_softmax': sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) / self.temperature data.update({'teacher_matrix': sim_matrix}) #if mask_c0 is not None: # sim_matrix.masked_fill_( # ~(mask_c0[..., None] * mask_c1[:, None]).bool(), # -INF) conf_matrix = F.softmax(sim_matrix, 1) * F.softmax(sim_matrix, 2) elif self.match_type == 'sinkhorn': # sinkhorn, dustbin included sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) #if mask_c0 is not None: # sim_matrix[:, :L, :S].masked_fill_( # ~(mask_c0[..., None] * mask_c1[:, None]).bool(), # -INF) # build uniform prior & use sinkhorn log_assign_matrix = self.log_optimal_transport( sim_matrix, self.bin_score, self.skh_iters) assign_matrix = log_assign_matrix.exp() conf_matrix = assign_matrix[:, :-1, :-1] # filter prediction with dustbin score (only in evaluation mode) if not self.training and self.skh_prefilter: filter0 = (assign_matrix.max(dim=2)[1] == S)[:, :-1] # [N, L] filter1 = (assign_matrix.max(dim=1)[1] == L)[:, :-1] # [N, S] conf_matrix[filter0[..., None].repeat(1, 1, S)] = 0 conf_matrix[filter1[:, None].repeat(1, L, 1)] = 0 if self.config['sparse_spvs']: data.update({'conf_matrix_with_bin': assign_matrix.clone()}) data.update({'conf_matrix_t': conf_matrix}) # predict coarse matches from conf_matrix #data.update(**self.get_coarse_match(conf_matrix, data)) @torch.no_grad() def get_coarse_match(self, conf_matrix, data): """ Args: conf_matrix (torch.Tensor): [N, L, S] data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c'] Returns: coarse_matches (dict): { 'b_ids' (torch.Tensor): [M'], 'i_ids' (torch.Tensor): [M'], 'j_ids' (torch.Tensor): [M'], 'gt_mask' (torch.Tensor): [M'], 'm_bids' (torch.Tensor): [M], 'mkpts0_c' (torch.Tensor): [M, 2], 'mkpts1_c' (torch.Tensor): [M, 2], 'mconf' (torch.Tensor): [M]} """ axes_lengths = { 'h0c': data['hw0_c'][0], 'w0c': data['hw0_c'][1], 'h1c': data['hw1_c'][0], 'w1c': data['hw1_c'][1] } _device = conf_matrix.device # 1. confidence thresholding mask = conf_matrix > self.thr mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', **axes_lengths) if 'mask0' not in data: mask_border(mask, self.border_rm, False) else: mask_border_with_padding(mask, self.border_rm, False, data['mask0'], data['mask1']) mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', **axes_lengths) # 2. mutual nearest mask = mask \ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) # 3. find all valid coarse matches # this only works when at most one `True` in each row mask_v, all_j_ids = mask.max(dim=2) b_ids, i_ids = torch.where(mask_v) j_ids = all_j_ids[b_ids, i_ids] mconf = conf_matrix[b_ids, i_ids, j_ids] # 4. Random sampling of training samples for fine-level LoFTR # (optional) pad samples with gt coarse-level matches if self.training: # NOTE: # The sampling is performed across all pairs in a batch without manually balancing # #samples for fine-level increases w.r.t. batch_size if 'mask0' not in data: num_candidates_max = mask.size(0) * max( mask.size(1), mask.size(2)) else: num_candidates_max = compute_max_candidates( data['mask0'], data['mask1']) num_matches_train = int(num_candidates_max * self.train_coarse_percent) num_matches_pred = len(b_ids) assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches" # pred_indices is to select from prediction if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min: pred_indices = torch.arange(num_matches_pred, device=_device) else: pred_indices = torch.randint( num_matches_pred, (num_matches_train - self.train_pad_num_gt_min, ), device=_device) # gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200) gt_pad_indices = torch.randint( len(data['spv_b_ids']), (max(num_matches_train - num_matches_pred, self.train_pad_num_gt_min), ), device=_device) mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero #print(len(data['spv_b_ids']),b_ids.size(),mconf.size(),gt_pad_indices.size(),pred_indices.size()) #print([j_ids, data['spv_j_ids']]) #print(j_ids.size(), data['spv_j_ids'].size()) b_ids, i_ids, j_ids, mconf = map( lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], dim=0), *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']], [j_ids, data['spv_j_ids']], [mconf, mconf_gt])) #print(b_ids.size(),'---------here') # These matches select patches that feed into fine-level network coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids} # 4. Update with matches in original image resolution scale = data['hw0_i'][0] / data['hw0_c'][0] scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale mkpts0_c = torch.stack( [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], dim=1) * scale0 mkpts1_c = torch.stack( [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], dim=1) * scale1 # These matches is the current prediction (for visualization) coarse_matches.update({ 'gt_mask': mconf == 0, 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches 'mkpts0_c': mkpts0_c[mconf != 0], 'mkpts1_c': mkpts1_c[mconf != 0], 'mconf': mconf[mconf != 0] }) return coarse_matches

py

3DG-STFM

3DG-STFM-master/src/optimizers/__init__.py

import torch from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR def build_optimizer(model, config): name = config.TRAINER.OPTIMIZER lr = config.TRAINER.TRUE_LR if name == "adam": return torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY) elif name == "adamw": #print('Heree is the here') for name, p in model.named_parameters(): if p.requires_grad==True: print(name) return torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY) else: raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!") def build_scheduler(config, optimizer): """ Returns: scheduler (dict):{ 'scheduler': lr_scheduler, 'interval': 'step', # or 'epoch' 'monitor': 'val_f1', (optional) 'frequency': x, (optional) } """ scheduler = {'interval': config.TRAINER.SCHEDULER_INTERVAL} name = config.TRAINER.SCHEDULER if name == 'MultiStepLR': scheduler.update( {'scheduler': MultiStepLR(optimizer, config.TRAINER.MSLR_MILESTONES, gamma=config.TRAINER.MSLR_GAMMA)}) elif name == 'CosineAnnealing': scheduler.update( {'scheduler': CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}) elif name == 'ExponentialLR': scheduler.update( {'scheduler': ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}) else: raise NotImplementedError() return scheduler

py

3DG-STFM

3DG-STFM-master/src/utils/comm.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ [Copied from detectron2] This file contains primitives for multi-gpu communication. This is useful when doing distributed training. """ import functools import logging import numpy as np import pickle import torch import torch.distributed as dist _LOCAL_PROCESS_GROUP = None """ A torch process group which only includes processes that on the same machine as the current process. This variable is set when processes are spawned by `launch()` in "engine/launch.py". """ def get_world_size() -> int: if not dist.is_available(): return 1 if not dist.is_initialized(): return 1 return dist.get_world_size() def get_rank() -> int: if not dist.is_available(): return 0 if not dist.is_initialized(): return 0 return dist.get_rank() def get_local_rank() -> int: """ Returns: The rank of the current process within the local (per-machine) process group. """ if not dist.is_available(): return 0 if not dist.is_initialized(): return 0 assert _LOCAL_PROCESS_GROUP is not None return dist.get_rank(group=_LOCAL_PROCESS_GROUP) def get_local_size() -> int: """ Returns: The size of the per-machine process group, i.e. the number of processes per machine. """ if not dist.is_available(): return 1 if not dist.is_initialized(): return 1 return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) def is_main_process() -> bool: return get_rank() == 0 def synchronize(): """ Helper function to synchronize (barrier) among all processes when using distributed training """ if not dist.is_available(): return if not dist.is_initialized(): return world_size = dist.get_world_size() if world_size == 1: return dist.barrier() @functools.lru_cache() def _get_global_gloo_group(): """ Return a process group based on gloo backend, containing all the ranks The result is cached. """ if dist.get_backend() == "nccl": return dist.new_group(backend="gloo") else: return dist.group.WORLD def _serialize_to_tensor(data, group): backend = dist.get_backend(group) assert backend in ["gloo", "nccl"] device = torch.device("cpu" if backend == "gloo" else "cuda") buffer = pickle.dumps(data) if len(buffer) > 1024 ** 3: logger = logging.getLogger(__name__) logger.warning( "Rank {} trying to all-gather {:.2f} GB of data on device {}".format( get_rank(), len(buffer) / (1024 ** 3), device ) ) storage = torch.ByteStorage.from_buffer(buffer) tensor = torch.ByteTensor(storage).to(device=device) return tensor def _pad_to_largest_tensor(tensor, group): """ Returns: list[int]: size of the tensor, on each rank Tensor: padded tensor that has the max size """ world_size = dist.get_world_size(group=group) assert ( world_size >= 1 ), "comm.gather/all_gather must be called from ranks within the given group!" local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device) size_list = [ torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size) ] dist.all_gather(size_list, local_size, group=group) size_list = [int(size.item()) for size in size_list] max_size = max(size_list) # we pad the tensor because torch all_gather does not support # gathering tensors of different shapes if local_size != max_size: padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device) tensor = torch.cat((tensor, padding), dim=0) return size_list, tensor def all_gather(data, group=None): """ Run all_gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object group: a torch process group. By default, will use a group which contains all ranks on gloo backend. Returns: list[data]: list of data gathered from each rank """ if get_world_size() == 1: return [data] if group is None: group = _get_global_gloo_group() if dist.get_world_size(group) == 1: return [data] tensor = _serialize_to_tensor(data, group) size_list, tensor = _pad_to_largest_tensor(tensor, group) max_size = max(size_list) # receiving Tensor from all ranks tensor_list = [ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list ] dist.all_gather(tensor_list, tensor, group=group) data_list = [] for size, tensor in zip(size_list, tensor_list): buffer = tensor.cpu().numpy().tobytes()[:size] data_list.append(pickle.loads(buffer)) return data_list def gather(data, dst=0, group=None): """ Run gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object dst (int): destination rank group: a torch process group. By default, will use a group which contains all ranks on gloo backend. Returns: list[data]: on dst, a list of data gathered from each rank. Otherwise, an empty list. """ if get_world_size() == 1: return [data] if group is None: group = _get_global_gloo_group() if dist.get_world_size(group=group) == 1: return [data] rank = dist.get_rank(group=group) tensor = _serialize_to_tensor(data, group) size_list, tensor = _pad_to_largest_tensor(tensor, group) # receiving Tensor from all ranks if rank == dst: max_size = max(size_list) tensor_list = [ torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list ] dist.gather(tensor, tensor_list, dst=dst, group=group) data_list = [] for size, tensor in zip(size_list, tensor_list): buffer = tensor.cpu().numpy().tobytes()[:size] data_list.append(pickle.loads(buffer)) return data_list else: dist.gather(tensor, [], dst=dst, group=group) return [] def shared_random_seed(): """ Returns: int: a random number that is the same across all workers. If workers need a shared RNG, they can use this shared seed to create one. All workers must call this function, otherwise it will deadlock. """ ints = np.random.randint(2 ** 31) all_ints = all_gather(ints) return all_ints[0] def reduce_dict(input_dict, average=True): """ Reduce the values in the dictionary from all processes so that process with rank 0 has the reduced results. Args: input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. average (bool): whether to do average or sum Returns: a dict with the same keys as input_dict, after reduction. """ world_size = get_world_size() if world_size < 2: return input_dict with torch.no_grad(): names = [] values = [] # sort the keys so that they are consistent across processes for k in sorted(input_dict.keys()): names.append(k) values.append(input_dict[k]) values = torch.stack(values, dim=0) dist.reduce(values, dst=0) if dist.get_rank() == 0 and average: # only main process gets accumulated, so only divide by # world_size in this case values /= world_size reduced_dict = {k: v for k, v in zip(names, values)} return reduced_dict

py

3DG-STFM

3DG-STFM-master/src/utils/misc.py

import os import contextlib import joblib from typing import Union from loguru import _Logger, logger from itertools import chain import torch from yacs.config import CfgNode as CN from pytorch_lightning.utilities import rank_zero_only def lower_config(yacs_cfg): if not isinstance(yacs_cfg, CN): return yacs_cfg return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} def upper_config(dict_cfg): if not isinstance(dict_cfg, dict): return dict_cfg return {k.upper(): upper_config(v) for k, v in dict_cfg.items()} def log_on(condition, message, level): if condition: assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL'] logger.log(level, message) def get_rank_zero_only_logger(logger: _Logger): if rank_zero_only.rank == 0: return logger else: for _level in logger._core.levels.keys(): level = _level.lower() setattr(logger, level, lambda x: None) logger._log = lambda x: None return logger def setup_gpus(gpus: Union[str, int]) -> int: """ A temporary fix for pytorch-lighting 1.3.x """ gpus = str(gpus) gpu_ids = [] if ',' not in gpus: n_gpus = int(gpus) return n_gpus if n_gpus != -1 else torch.cuda.device_count() else: gpu_ids = [i.strip() for i in gpus.split(',') if i != ''] # setup environment variables visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') if visible_devices is None: os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids) visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}') else: logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.') return len(gpu_ids) def flattenList(x): return list(chain(*x)) @contextlib.contextmanager def tqdm_joblib(tqdm_object): """Context manager to patch joblib to report into tqdm progress bar given as argument Usage: with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing) ret_vals = Parallel(n_jobs=args.world_size)( delayed(lambda x: _compute_cov_score(pid, *x))(param) for param in tqdm(combinations(image_ids, 2), desc=f'Computing cov_score of [{pid}]', total=len(image_ids)*(len(image_ids)-1)/2)) Src: https://stackoverflow.com/a/58936697 """ class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def __call__(self, *args, **kwargs): tqdm_object.update(n=self.batch_size) return super().__call__(*args, **kwargs) old_batch_callback = joblib.parallel.BatchCompletionCallBack joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback try: yield tqdm_object finally: joblib.parallel.BatchCompletionCallBack = old_batch_callback tqdm_object.close()

py

3DG-STFM

3DG-STFM-master/src/utils/dataset.py

import io from loguru import logger import cv2 import numpy as np import h5py import torch from numpy.linalg import inv import os try: # for internel use only from .client import MEGADEPTH_CLIENT, SCANNET_CLIENT except Exception: MEGADEPTH_CLIENT = SCANNET_CLIENT = None # --- DATA IO --- def load_array_from_s3( path, client, cv_type, use_h5py=False, ): byte_str = client.Get(path) try: if not use_h5py: raw_array = np.fromstring(byte_str, np.uint8) data = cv2.imdecode(raw_array, cv_type) else: f = io.BytesIO(byte_str) data = np.array(h5py.File(f, 'r')['/depth']) except Exception as ex: print(f"==> Data loading failure: {path}") raise ex assert data is not None return data def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT): cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \ else cv2.IMREAD_COLOR if str(path).startswith('s3://'): image = load_array_from_s3(str(path), client, cv_type) else: image = cv2.imread(str(path), cv_type) #image =image*12. #image=image/float(image.max()) #image*=255. if augment_fn is not None: image = cv2.imread(str(path), cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = augment_fn(image) image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) return image # (h, w) def get_resized_wh(w, h, resize=None): if resize is not None: # resize the longer edge scale = resize / max(h, w) w_new, h_new = int(round(w*scale)), int(round(h*scale)) else: w_new, h_new = w, h return w_new, h_new def get_divisible_wh(w, h, df=None): if df is not None: w_new, h_new = map(lambda x: int(x // df * df), [w, h]) else: w_new, h_new = w, h return w_new, h_new def pad_bottom_right(inp, pad_size, ret_mask=False): assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}" mask = None if inp.ndim == 2: padded = np.zeros((pad_size, pad_size), dtype=inp.dtype) padded[:inp.shape[0], :inp.shape[1]] = inp if ret_mask: mask = np.zeros((pad_size, pad_size), dtype=bool) mask[:inp.shape[0], :inp.shape[1]] = True elif inp.ndim == 3: padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype) padded[:, :inp.shape[1], :inp.shape[2]] = inp if ret_mask: mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool) mask[:, :inp.shape[1], :inp.shape[2]] = True else: raise NotImplementedError() return padded, mask # --- MEGADEPTH --- def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None): """ Args: resize (int, optional): the longer edge of resized images. None for no resize. padding (bool): If set to 'True', zero-pad resized images to squared size. augment_fn (callable, optional): augments images with pre-defined visual effects Returns: image (torch.tensor): (1, h, w) mask (torch.tensor): (h, w) scale (torch.tensor): [w/w_new, h/h_new] """ # read image image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) # resize image w, h = image.shape[1], image.shape[0] w_new, h_new = get_resized_wh(w, h, resize) w_new, h_new = get_divisible_wh(w_new, h_new, df) image = cv2.resize(image, (w_new, h_new)) scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float) if padding: # padding pad_to = max(h_new, w_new) image, mask = pad_bottom_right(image, pad_to, ret_mask=True) else: mask = None image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized mask = torch.from_numpy(mask) return image, mask, scale def read_megadepth_rgb(path, resize=None, df=None, padding=False, augment_fn=None): """ Args: resize (int, optional): the longer edge of resized images. None for no resize. padding (bool): If set to 'True', zero-pad resized images to squared size. augment_fn (callable, optional): augments images with pre-defined visual effects Returns: image (torch.tensor): (1, h, w) mask (torch.tensor): (h, w) scale (torch.tensor): [w/w_new, h/h_new] """ image = cv2.imread(str(path), cv2.IMREAD_COLOR) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) #image = cv2.resize(image, resize) image = np.ascontiguousarray(image) mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) image= image.astype(float) image[:, :, 0] = (image[:, :, 0]/255.-mean[0]) / std[0] image[:, :, 1] = (image[:, :, 1]/255.-mean[1]) / std[1] image[:, :, 2] = (image[:, :, 2]/255.-mean[2]) / std[2] # (h, w) -> (1, h, w) and normalized #image = torch.from_numpy(image).float()[None] #return image # read image #image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) # resize image w, h = image.shape[1], image.shape[0] w_new, h_new = get_resized_wh(w, h, resize) w_new, h_new = get_divisible_wh(w_new, h_new, df) image = cv2.resize(image, (w_new, h_new)) scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float) image = image.transpose(2, 0, 1) if padding: # padding pad_to = max(h_new, w_new) image, mask = pad_bottom_right(image, pad_to, ret_mask=True) else: mask = None image = torch.from_numpy(image).float() # (3, h, w) and normalized mask = torch.from_numpy(mask) return image, mask, scale def read_megadepth_depth(path, pad_to=None): if str(path).startswith('s3://'): depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True) else: depth = np.array(h5py.File(path, 'r')['depth']) if pad_to is not None: depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False) depth = torch.from_numpy(depth).float() # (h, w) return depth # --- ScanNet --- def read_scannet_gray(path, resize=(640, 480), augment_fn=None): """ Args: resize (tuple): align image to depthmap, in (w, h). augment_fn (callable, optional): augments images with pre-defined visual effects Returns: image (torch.tensor): (1, h, w) mask (torch.tensor): (h, w) scale (torch.tensor): [w/w_new, h/h_new] """ # read and resize image image = imread_gray(path, augment_fn) image = cv2.resize(image, resize) # (h, w) -> (1, h, w) and normalized image = torch.from_numpy(image).float()[None] / 255 return image def read_scannet_rgb(path, resize=(640, 480), augment_fn=None): """ Args: resize (tuple): align image to depthmap, in (w, h). augment_fn (callable, optional): augments images with pre-defined visual effects Returns: image (torch.tensor): (1, h, w) mask (torch.tensor): (h, w) scale (torch.tensor): [w/w_new, h/h_new] """ # read and resize image image = cv2.imread(str(path), cv2.IMREAD_COLOR) #if os.path.exists(str(path)): # print('yes') #if os.path.exists(path): # print('no') #print(str(path)) #print(image) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) image = cv2.resize(image, resize) image = np.ascontiguousarray(image) mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) image= image.astype(float) image[:, :, 0] = (image[:, :, 0]/255.-mean[0]) / std[0] image[:, :, 1] = (image[:, :, 1]/255.-mean[1]) / std[1] image[:, :, 2] = (image[:, :, 2]/255.-mean[2]) / std[2] # (h, w) -> (1, h, w) and normalized image = torch.from_numpy(image).float()[None] return image def read_scannet_depth(path): if str(path).startswith('s3://'): depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED) else: depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) depth = depth / 1000 depth = torch.from_numpy(depth).float() # (h, w) return depth def read_scannet_pose(path): """ Read ScanNet's Camera2World pose and transform it to World2Camera. Returns: pose_w2c (np.ndarray): (4, 4) """ cam2world = np.loadtxt(path, delimiter=' ') world2cam = inv(cam2world) return world2cam def read_scannet_intrinsic(path): """ Read ScanNet's intrinsic matrix and return the 3x3 matrix. """ intrinsic = np.loadtxt(path, delimiter=' ') return intrinsic[:-1, :-1]

py

3DG-STFM

3DG-STFM-master/src/utils/metrics.py

import torch import cv2 import numpy as np from collections import OrderedDict from loguru import logger from kornia.geometry.epipolar import numeric from kornia.geometry.conversions import convert_points_to_homogeneous import random # --- METRICS --- def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0): # angle error between 2 vectors t_gt = T_0to1[:3, 3] n = np.linalg.norm(t) * np.linalg.norm(t_gt) t_err = np.rad2deg(np.arccos(np.clip(np.dot(t, t_gt) / n, -1.0, 1.0))) t_err = np.minimum(t_err, 180 - t_err) # handle E ambiguity if np.linalg.norm(t_gt) < ignore_gt_t_thr: # pure rotation is challenging t_err = 0 # angle error between 2 rotation matrices R_gt = T_0to1[:3, :3] cos = (np.trace(np.dot(R.T, R_gt)) - 1) / 2 cos = np.clip(cos, -1., 1.) # handle numercial errors R_err = np.rad2deg(np.abs(np.arccos(cos))) return t_err, R_err def symmetric_epipolar_distance(pts0, pts1, E, K0, K1): """Squared symmetric epipolar distance. This can be seen as a biased estimation of the reprojection error. Args: pts0 (torch.Tensor): [N, 2] E (torch.Tensor): [3, 3] """ pts0 = (pts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] pts1 = (pts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] pts0 = convert_points_to_homogeneous(pts0) pts1 = convert_points_to_homogeneous(pts1) Ep0 = pts0 @ E.T # [N, 3] p1Ep0 = torch.sum(pts1 * Ep0, -1) # [N,] Etp1 = pts1 @ E # [N, 3] d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2) + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2)) # N return d def compute_symmetrical_epipolar_errors(data): """ Update: data (dict):{"epi_errs": [M]} """ Tx = numeric.cross_product_matrix(data['T_0to1'][:, :3, 3]) E_mat = Tx @ data['T_0to1'][:, :3, :3] m_bids = data['m_bids'] pts0 = data['mkpts0_f'] pts1 = data['mkpts1_f'] epi_errs = [] for bs in range(Tx.size(0)): mask = m_bids == bs epi_errs.append( symmetric_epipolar_distance(pts0[mask], pts1[mask], E_mat[bs], data['K0'][bs], data['K1'][bs])) epi_errs = torch.cat(epi_errs, dim=0) data.update({'epi_errs': epi_errs}) def estimate_homo(kpts0, kpts1, M, thresh, conf=0.99999): if len(kpts0) < 5: return None # normalize keypoints kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] # normalize ransac threshold ransac_thr = thresh / np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]]) # compute pose with cv2 E, mask = cv2.findEssentialMat( kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC) if E is None: print("\nE is None while trying to recover pose.\n") return None # recover pose from E best_num_inliers = 0 ret = None for _E in np.split(E, len(E) / 3): n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) if n > best_num_inliers: ret = (R, t[:, 0], mask.ravel() > 0) best_num_inliers = n return ret def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999): if len(kpts0) < 5: return None # normalize keypoints kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] # normalize ransac threshold ransac_thr = thresh / np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]]) # compute pose with cv2 E, mask = cv2.findEssentialMat( kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC) #E, mask = cv2.findEssentialMat( # kpts0, kpts1, np.eye(3), prob=conf, method=None) #E, mask = cv2.findEssentialMat( # kpts0, kpts1, np.eye(3), prob=conf, method=None) if E is None: print("\nE is None while trying to recover pose.\n") return None # recover pose from E best_num_inliers = 0 ret = None for _E in np.split(E, len(E) / 3): n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) if n > best_num_inliers: ret = (R, t[:, 0], mask.ravel() > 0) best_num_inliers = n return ret def compute_homo_errors(data, config): """ Update: data (dict):{ "inliers" List[np.ndarray]: [N] } """ pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 conf = config.TRAINER.RANSAC_CONF # 0.99999 data.update({'inliers': []}) data.update({'epi_errs': []}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu().numpy() pts1 = data['mkpts1_f'].cpu().numpy() M=data['M'] #print(data) #K0 = data['K0'].cpu().numpy() #K1 = data['K1'].cpu().numpy() #T_0to1 = data['T_0to1'].cpu().numpy() for bs in range(data['image0'].shape[0]): mask = m_bids == bs kpts0 = pts0[mask] kpts1 = pts1[mask] M_b = M[bs] if kpts0.shape[0]==0: data['inliers'].append(np.array([]).astype(np.bool)) data['epi_errs'].append(np.array([]).astype(np.bool)) else: kpts0 = kpts0.reshape((1, -1, 2)) kpts0 = cv2.perspectiveTransform(kpts0, M_b.cpu().numpy()) inliers=0 epi_errs = [] for ii,cord in enumerate(kpts0[0]): diff = cord-kpts1[ii] if (diff[0]**2+diff[1]**2)<=4: inliers+=1 epi_errs.append(np.sqrt(diff[0]**2+diff[1]**2)) data['epi_errs'].append(np.array(epi_errs)) data['inliers'].append(inliers) def filter_based_on_depth(depth0,depth1,coordinates0,coordinates1,K0,K1,T_0to1): coordinates0=coordinates0[None,...] coordinates1 = coordinates1[None, ...] coordinates0 =coordinates0.long() coordinates1 =coordinates1.long() kpts0_depth = torch.stack([depth0[coordinates0[0,:, 1], coordinates0[0,:, 0]]], dim=0) nonzero_mask = (kpts0_depth != 0)*float('inf') kpts0_h = torch.cat([coordinates0, torch.ones_like(coordinates0[:, :, [0]])], dim=-1) * kpts0_depth[ ..., None] # (N, L, 3) kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) # Rigid Transform w_kpts0_cam = T_0to1[:3, :3] @ kpts0_cam + T_0to1[:3, [3]] # (N, 3, L) w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] # Project w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth # Covisible Check h, w = depth1.shape[0:2] covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w - 1) * \ (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h - 1) w_kpts0_long = w_kpts0.long() w_kpts0_long[~covisible_mask, :] = 0 w_kpts0_depth = torch.stack([depth1[coordinates1[0, :, 1], coordinates1[0, :, 0]]], dim=0) # consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 #diff = (abs(w_kpts0_depth - w_kpts0_depth_computed)/(w_kpts0_depth+1e-4)) diff = abs((w_kpts0_depth - w_kpts0_depth_computed)/(w_kpts0_depth+1e-4)) #diff *= nonzero_mask indice = torch.where(diff>0.15) #print(diff.size()) #print(len(indice[1])) new_cor0 = coordinates0[indice[0],indice[1]] new_cor1 = coordinates1[indice[0],indice[1]] return indice[1]#new_cor0,new_cor1 def filter_depth_inconsist_point(data, config): """ Update: data (dict):{ "R_errs" List[float]: [N] "t_errs" List[float]: [N] "inliers" List[np.ndarray]: [N] } """ pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 conf = config.TRAINER.RANSAC_CONF # 0.99999 data.update({'R_errs': [], 't_errs': [], 'inliers': []}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu()#.numpy() pts1 = data['mkpts1_f'].cpu()#.numpy() depth0 = data['depth0'].cpu()#.numpy() depth1 = data['depth1'].cpu()#.numpy()# shape (1,480,640) K0 = data['K0'].cpu()#.numpy() K1 = data['K1'].cpu()#.numpy() T_0to1 = data['T_0to1'].cpu()#.numpy() for bs in range(K0.shape[0]): mask = m_bids == bs #ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) ind=filter_based_on_depth(depth0[bs],depth1[bs],pts0, pts1, K0[bs], K1[bs],T_0to1[bs]) m_bids_new = data['m_bids'] m_bids_new[ind]=-1 data.update({'m_bids': m_bids_new.cuda()}) #data.update({'mkpts0_f': new_cor0.cuda(), 'mkpts1_f': new_cor1.cuda(),'m_bids': m_bids_new.cuda()}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu().numpy() mask = m_bids == bs pts1 = data['mkpts1_f'].cpu().numpy() K0 = data['K0'].cpu().numpy() K1 = data['K1'].cpu().numpy() T_0to1 = data['T_0to1'].cpu().numpy() ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) if ret is None: data['R_errs'].append(np.inf) data['t_errs'].append(np.inf) data['inliers'].append(np.array([]).astype(np.bool)) else: R, t, inliers = ret t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) data['R_errs'].append(R_err) data['t_errs'].append(t_err) data['inliers'].append(inliers) def filter_based_random_sample(depth0,depth1,pts0, pts1): max_depth = depth0.max() h, w = depth0.shape[0:2] scale =8 h = h//8 w = w//8 uni_pb = 1./float(h*w*10000) total = pts0.size(0) rest = 1 - uni_pb*total set_ind = np.arange(total+1) pb_ind = [uni_pb]*total+[rest] np.random.seed() ind = np.random.choice(set_ind,size = (int(total/5)),replace=False, p = pb_ind) dust_bin = np.where(ind==total)[0] try: ind =list(ind) ind.pop(dust_bin[0]) return ind except: return ind def filter_unsampled_point(data, config): """ Update: data (dict):{ "R_errs" List[float]: [N] "t_errs" List[float]: [N] "inliers" List[np.ndarray]: [N] } """ pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 conf = config.TRAINER.RANSAC_CONF # 0.99999 data.update({'R_errs': [], 't_errs': [], 'inliers': []}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu()#.numpy() pts1 = data['mkpts1_f'].cpu()#.numpy() depth0 = data['depth0'].cpu()#.numpy() depth1 = data['depth1'].cpu()#.numpy()# shape (1,480,640) K0 = data['K0'].cpu()#.numpy() K1 = data['K1'].cpu()#.numpy() T_0to1 = data['T_0to1'].cpu()#.numpy() for bs in range(K0.shape[0]): mask = m_bids == bs #ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) ind=filter_based_random_sample(depth0[bs],depth1[bs],pts0, pts1) m_bids_new = data['m_bids'] m_bids_new[ind]=-1 data.update({'m_bids': m_bids_new.cuda()}) #data.update({'mkpts0_f': new_cor0.cuda(), 'mkpts1_f': new_cor1.cuda(),'m_bids': m_bids_new.cuda()}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu().numpy() mask = m_bids == bs pts1 = data['mkpts1_f'].cpu().numpy() K0 = data['K0'].cpu().numpy() K1 = data['K1'].cpu().numpy() T_0to1 = data['T_0to1'].cpu().numpy() ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) if ret is None: data['R_errs'].append(np.inf) data['t_errs'].append(np.inf) data['inliers'].append(np.array([]).astype(np.bool)) else: R, t, inliers = ret t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) data['R_errs'].append(R_err) data['t_errs'].append(t_err) data['inliers'].append(inliers) def compute_pose_errors(data, config): """ Update: data (dict):{ "R_errs" List[float]: [N] "t_errs" List[float]: [N] "inliers" List[np.ndarray]: [N] } """ pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 conf = config.TRAINER.RANSAC_CONF # 0.99999 data.update({'R_errs': [], 't_errs': [], 'inliers': []}) m_bids = data['m_bids'].cpu().numpy() pts0 = data['mkpts0_f'].cpu().numpy() pts1 = data['mkpts1_f'].cpu().numpy() K0 = data['K0'].cpu().numpy() K1 = data['K1'].cpu().numpy() T_0to1 = data['T_0to1'].cpu().numpy() for bs in range(K0.shape[0]): mask = m_bids == bs ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) if ret is None: data['R_errs'].append(np.inf) data['t_errs'].append(np.inf) data['inliers'].append(np.array([]).astype(np.bool)) else: R, t, inliers = ret t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) data['R_errs'].append(R_err) data['t_errs'].append(t_err) data['inliers'].append(inliers) # --- METRIC AGGREGATION --- def error_auc(errors, thresholds): """ Args: errors (list): [N,] thresholds (list) """ errors = [0] + sorted(list(errors)) recall = list(np.linspace(0, 1, len(errors))) aucs = [] thresholds = [5, 10, 20] for thr in thresholds: last_index = np.searchsorted(errors, thr) y = recall[:last_index] + [recall[last_index-1]] x = errors[:last_index] + [thr] aucs.append(np.trapz(y, x) / thr) return {f'auc@{t}': auc for t, auc in zip(thresholds, aucs)} def epidist_prec(errors, thresholds, ret_dict=False): precs = [] for thr in thresholds: prec_ = [] for errs in errors: correct_mask = errs < thr prec_.append(np.mean(correct_mask) if len(correct_mask) > 0 else 0) precs.append(np.mean(prec_) if len(prec_) > 0 else 0) if ret_dict: return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)} else: return precs def aggregate_metrics(metrics, epi_err_thr=5e-4): """ Aggregate metrics for the whole dataset: (This method should be called once per dataset) 1. AUC of the pose error (angular) at the threshold [5, 10, 20] 2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth) """ # filter duplicates unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers'])) unq_ids = list(unq_ids.values()) logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...') # pose auc angular_thresholds = [5, 10, 20] pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids] aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20) # matching precision dist_thresholds = [epi_err_thr] precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr) return {**aucs, **precs} def aggregate_metrics_homo(metrics, epi_err_thr=5e-4): """ Aggregate metrics for the whole dataset: (This method should be called once per dataset) 1. AUC of the pose error (angular) at the threshold [5, 10, 20] 2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth) """ # filter duplicates #unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers'])) #unq_ids = list(unq_ids.values()) #logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...') # pose auc #angular_thresholds = [5, 10, 20] #pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)#[unq_ids] #aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20) # matching precision dist_thresholds = [epi_err_thr] precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object), dist_thresholds, True) # (prec@err_thr) return { **precs}

py

3DG-STFM

3DG-STFM-master/src/utils/profiler.py

import torch from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler from contextlib import contextmanager from pytorch_lightning.utilities import rank_zero_only class InferenceProfiler(SimpleProfiler): """ This profiler records duration of actions with cuda.synchronize() Use this in test time. """ def __init__(self): super().__init__() self.start = rank_zero_only(self.start) self.stop = rank_zero_only(self.stop) self.summary = rank_zero_only(self.summary) @contextmanager def profile(self, action_name: str) -> None: try: torch.cuda.synchronize() self.start(action_name) yield action_name finally: torch.cuda.synchronize() self.stop(action_name) def build_profiler(name): if name == 'inference': return InferenceProfiler() elif name == 'pytorch': from pytorch_lightning.profiler import PyTorchProfiler return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100) elif name is None: return PassThroughProfiler() else: raise ValueError(f'Invalid profiler: {name}')

py

3DG-STFM

3DG-STFM-master/src/losses/loftr_loss.py

from loguru import logger import torch import torch.nn as nn import torch.nn.functional as F class LoFTRLoss(nn.Module): def __init__(self, config): super().__init__() self.config = config # config under the global namespace self.loss_config = config['loftr']['loss'] self.match_type = self.config['loftr']['match_coarse']['match_type'] self.sparse_spvs = self.config['loftr']['match_coarse']['sparse_spvs'] # coarse-level self.correct_thr = self.loss_config['fine_correct_thr'] self.c_pos_w = self.loss_config['pos_weight'] self.c_neg_w = self.loss_config['neg_weight'] # fine-level self.fine_type = self.loss_config['fine_type'] def compute_coarse_loss(self, conf, conf_gt, weight=None): """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt. Args: conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1) conf_gt (torch.Tensor): (N, HW0, HW1) weight (torch.Tensor): (N, HW0, HW1) """ pos_mask, neg_mask = conf_gt == 1, conf_gt == 0 c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w # corner case: no gt coarse-level match at all if not pos_mask.any(): # assign a wrong gt pos_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. c_pos_w = 0. if not neg_mask.any(): neg_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. c_neg_w = 0. if self.loss_config['coarse_type'] == 'cross_entropy': assert not self.sparse_spvs, 'Sparse Supervision for cross-entropy not implemented!' conf = torch.clamp(conf, 1e-6, 1-1e-6) loss_pos = - torch.log(conf[pos_mask]) loss_neg = - torch.log(1 - conf[neg_mask]) if weight is not None: loss_pos = loss_pos * weight[pos_mask] loss_neg = loss_neg * weight[neg_mask] return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() elif self.loss_config['coarse_type'] == 'focal': conf = torch.clamp(conf, 1e-6, 1-1e-6) alpha = self.loss_config['focal_alpha'] gamma = self.loss_config['focal_gamma'] if self.sparse_spvs: pos_conf = conf[:, :-1, :-1][pos_mask] \ if self.match_type == 'sinkhorn' \ else conf[pos_mask] loss_pos = - alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log() # calculate losses for negative samples if self.match_type == 'sinkhorn': neg0, neg1 = conf_gt.sum(-1) == 0, conf_gt.sum(1) == 0 neg_conf = torch.cat([conf[:, :-1, -1][neg0], conf[:, -1, :-1][neg1]], 0) loss_neg = - alpha * torch.pow(1 - neg_conf, gamma) * neg_conf.log() else: # These is no dustbin for dual_softmax, so we left unmatchable patches without supervision. # we could also add 'pseudo negtive-samples' pass # handle loss weights if weight is not None: # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out, # but only through manually setting corresponding regions in sim_matrix to '-inf'. loss_pos = loss_pos * weight[pos_mask] if self.match_type == 'sinkhorn': neg_w0 = (weight.sum(-1) != 0)[neg0] neg_w1 = (weight.sum(1) != 0)[neg1] neg_mask = torch.cat([neg_w0, neg_w1], 0) loss_neg = loss_neg[neg_mask] loss = c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() \ if self.match_type == 'sinkhorn' \ else c_pos_w * loss_pos.mean() return loss # positive and negative elements occupy similar propotions. => more balanced loss weights needed else: # dense supervision (in the case of match_type=='sinkhorn', the dustbin is not supervised.) loss_pos = - alpha * torch.pow(1 - conf[pos_mask], gamma) * (conf[pos_mask]).log() loss_neg = - alpha * torch.pow(conf[neg_mask], gamma) * (1 - conf[neg_mask]).log() if weight is not None: loss_pos = loss_pos * weight[pos_mask] loss_neg = loss_neg * weight[neg_mask] return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() # each negative element occupy a smaller propotion than positive elements. => higher negative loss weight needed else: raise ValueError('Unknown coarse loss: {type}'.format(type=self.loss_config['coarse_type'])) def compute_fine_loss(self, expec_f, expec_f_gt): if self.fine_type == 'l2_with_std': return self._compute_fine_loss_l2_std(expec_f, expec_f_gt) elif self.fine_type == 'l2': return self._compute_fine_loss_l2(expec_f, expec_f_gt) else: raise NotImplementedError() def _compute_fine_loss_l2(self, expec_f, expec_f_gt): """ Args: expec_f (torch.Tensor): [M, 2] <x, y> expec_f_gt (torch.Tensor): [M, 2] <x, y> """ correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr if correct_mask.sum() == 0: if self.training: # this seldomly happen when training, since we pad prediction with gt logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True else: return None offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1) return offset_l2.mean() def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt): """ Args: expec_f (torch.Tensor): [M, 3] <x, y, std> expec_f_gt (torch.Tensor): [M, 2] <x, y> """ # correct_mask tells you which pair to compute fine-loss correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr # use std as weight that measures uncertainty std = expec_f[:, 2] inverse_std = 1. / torch.clamp(std, min=1e-10) weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std # corner case: no correct coarse match found if not correct_mask.any(): if self.training: # this seldomly happen during training, since we pad prediction with gt # sometimes there is not coarse-level gt at all. logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True weight[0] = 0. else: return None # l2 loss with std offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1) loss = (offset_l2 * weight[correct_mask]).mean() return loss @torch.no_grad() def compute_c_weight(self, data): """ compute element-wise weights for computing coarse-level loss. """ if 'mask0' in data: c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float() else: c_weight = None return c_weight def forward(self, data): """ Update: data (dict): update{ 'loss': [1] the reduced loss across a batch, 'loss_scalars' (dict): loss scalars for tensorboard_record } """ loss_scalars = {} # 0. compute element-wise loss weight c_weight = self.compute_c_weight(data) # 1. coarse-level loss loss_c = self.compute_coarse_loss( data['conf_matrix_with_bin'] if self.sparse_spvs and self.match_type == 'sinkhorn' \ else data['conf_matrix'], data['conf_matrix_gt'], weight=c_weight) loss = loss_c * self.loss_config['coarse_weight'] loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()}) # 2. fine-level loss loss_f = self.compute_fine_loss(data['expec_f'], data['expec_f_gt']) if loss_f is not None: loss += loss_f * self.loss_config['fine_weight'] loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()}) else: assert self.training is False loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound loss_scalars.update({'loss': loss.clone().detach().cpu()}) data.update({"loss": loss, "loss_scalars": loss_scalars}) class LoFTRLoss_t_s(nn.Module): ## Student teacher learning loss def __init__(self, config): super().__init__() self.config = config # config under the global namespace self.loss_config = config['loftr']['loss'] self.match_type = self.config['loftr']['match_coarse']['match_type'] self.sparse_spvs = self.config['loftr']['match_coarse']['sparse_spvs'] # coarse-level self.correct_thr = self.loss_config['fine_correct_thr'] self.c_pos_w = self.loss_config['pos_weight'] self.c_neg_w = self.loss_config['neg_weight'] # fine-level self.fine_type = self.loss_config['fine_type'] def compute_coarse_loss(self, conf, conf_gt, sim_s,sim_t,weight=None): """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt. Args: conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1) conf_gt (torch.Tensor): (N, HW0, HW1) weight (torch.Tensor): (N, HW0, HW1) """ T =2. loss_fea = nn.KLDivLoss(reduction='none') N = sim_s.size(0) c_loss1 = loss_fea(F.log_softmax(sim_s / T, dim=2), F.softmax(sim_t / T, dim=2)) * T * T c_loss2 = loss_fea(F.log_softmax(sim_s / T, dim=1), F.softmax(sim_t / T, dim=1)) * T * T pos_mask, neg_mask = conf_gt == 1, conf_gt == 0 c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w # corner case: no gt coarse-level match at all if not pos_mask.any(): # assign a wrong gt pos_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. c_pos_w = 0. if not neg_mask.any(): neg_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. c_neg_w = 0. gamma = self.loss_config['focal_gamma'] alpha = self.loss_config['focal_alpha'] loss_pos_1 = torch.pow(1 - conf[pos_mask], gamma) * c_loss1[pos_mask] loss_neg_1 = torch.pow(conf[neg_mask], gamma) * (1 - c_loss1[neg_mask]) loss_pos_2 = torch.pow(1 - conf[pos_mask], gamma) * c_loss2[pos_mask] loss_neg_2 = torch.pow(conf[neg_mask], gamma) * (1 - c_loss2[neg_mask]) c_loss_kd = (loss_pos_1.mean() +loss_neg_1.mean() +loss_pos_2.mean() +loss_neg_2.mean())/2.*alpha*16.#hard code, modified in the future alpha=0.25, total weights=0.25*16=4 if self.loss_config['coarse_type'] == 'cross_entropy': assert not self.sparse_spvs, 'Sparse Supervision for cross-entropy not implemented!' conf = torch.clamp(conf, 1e-6, 1 - 1e-6) loss_pos = - torch.log(conf[pos_mask]) loss_neg = - torch.log(1 - conf[neg_mask]) if weight is not None: loss_pos = loss_pos * weight[pos_mask] loss_neg = loss_neg * weight[neg_mask] return (c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean())*0.5+0.5*c_loss_kd elif self.loss_config['coarse_type'] == 'focal': conf = torch.clamp(conf, 1e-6, 1 - 1e-6) alpha = self.loss_config['focal_alpha'] gamma = self.loss_config['focal_gamma'] if self.sparse_spvs: pos_conf = conf[:, :-1, :-1][pos_mask] \ if self.match_type == 'sinkhorn' \ else conf[pos_mask] loss_pos = - alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log() # calculate losses for negative samples if self.match_type == 'sinkhorn': neg0, neg1 = conf_gt.sum(-1) == 0, conf_gt.sum(1) == 0 neg_conf = torch.cat([conf[:, :-1, -1][neg0], conf[:, -1, :-1][neg1]], 0) loss_neg = - alpha * torch.pow(1 - neg_conf, gamma) * neg_conf.log() else: # These is no dustbin for dual_softmax, so we left unmatchable patches without supervision. # we could also add 'pseudo negtive-samples' pass # handle loss weights if weight is not None: # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out, # but only through manually setting corresponding regions in sim_matrix to '-inf'. loss_pos = loss_pos * weight[pos_mask] if self.match_type == 'sinkhorn': neg_w0 = (weight.sum(-1) != 0)[neg0] neg_w1 = (weight.sum(1) != 0)[neg1] neg_mask = torch.cat([neg_w0, neg_w1], 0) loss_neg = loss_neg[neg_mask] loss = c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() \ if self.match_type == 'sinkhorn' \ else c_pos_w * loss_pos.mean() return loss*0.5+0.5*c_loss_kd # positive and negative elements occupy similar propotions. => more balanced loss weights needed else: # dense supervision (in the case of match_type=='sinkhorn', the dustbin is not supervised.) loss_pos = - alpha * torch.pow(1 - conf[pos_mask], gamma) * (conf[pos_mask]).log() loss_neg = - alpha * torch.pow(conf[neg_mask], gamma) * (1 - conf[neg_mask]).log() if weight is not None: loss_pos = loss_pos * weight[pos_mask] loss_neg = loss_neg * weight[neg_mask] return (c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean()) * 0.5 + 0.5 * c_loss_kd else: raise ValueError('Unknown coarse loss: {type}'.format(type=self.loss_config['coarse_type'])) def compute_fine_loss(self, expec_f, expec_f_gt): if self.fine_type == 'l2_with_std': return self._compute_fine_loss_l2_std(expec_f, expec_f_gt) elif self.fine_type == 'l2': return self._compute_fine_loss_l2(expec_f, expec_f_gt) else: raise NotImplementedError() def _compute_fine_loss_l2(self, expec_f, expec_f_gt): """ Args: expec_f (torch.Tensor): [M, 2] <x, y> expec_f_gt (torch.Tensor): [M, 2] <x, y> """ correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr if correct_mask.sum() == 0: if self.training: # this seldomly happen when training, since we pad prediction with gt logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True else: return None offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1) return offset_l2.mean() def _compute_fine_loss_t_s_l2(self, expec_f, expec_f_gt,expec_f_t): """ Args: expec_f (torch.Tensor): [M, 2] <x, y> expec_f_gt (torch.Tensor): [M, 2] <x, y> """ correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr if correct_mask.sum() == 0: if self.training: # this seldomly happen when training, since we pad prediction with gt logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True else: return None offset_l2 = ((expec_f_t[correct_mask,:2] - expec_f[correct_mask,:2]) ** 2).sum(-1) return offset_l2.mean() def _compute_fine_loss_t_s_kld(self, expec_f, expec_f_gt,expec_f_t): """ Attentive loss """ correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr if correct_mask.sum() == 0: if self.training: # this seldomly happen when training, since we pad prediction with gt logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True else: return None # use std as weight that measures uncertainty std1 = expec_f[correct_mask, 2].detach() std2 = expec_f_t[correct_mask, 2].detach() tmp =((expec_f_t[correct_mask,:2] - expec_f[correct_mask,:2]) ** 2).sum(-1) loss = (torch.log(std2)-torch.log(std1))+(std1**2+tmp)/2.*std2**2 -0.5 loss = loss.mean() return loss def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt): """ Args: expec_f (torch.Tensor): [M, 3] <x, y, std> expec_f_gt (torch.Tensor): [M, 2] <x, y> """ # correct_mask tells you which pair to compute fine-loss correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr # use std as weight that measures uncertainty std = expec_f[:, 2] inverse_std = 1. / torch.clamp(std, min=1e-10) weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std # corner case: no correct coarse match found if not correct_mask.any(): if self.training: # this seldomly happen during training, since we pad prediction with gt # sometimes there is not coarse-level gt at all. logger.warning("assign a false supervision to avoid ddp deadlock") correct_mask[0] = True weight[0] = 0. else: return None # l2 loss with std offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1) loss = (offset_l2 * weight[correct_mask]).mean() return loss @torch.no_grad() def compute_c_weight(self, data): """ compute element-wise weights for computing coarse-level loss. """ if 'mask0' in data: c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float() else: c_weight = None return c_weight def forward(self, data): """ Update: data (dict): update{ 'loss': [1] the reduced loss across a batch, 'loss_scalars' (dict): loss scalars for tensorboard_record } """ loss_scalars = {} # 0. compute element-wise loss weight c_weight = self.compute_c_weight(data) # 1. coarse-level loss loss_c = self.compute_coarse_loss(data['conf_matrix'],data['conf_matrix_gt'],data['sim_matrix'], data['teacher_matrix'],weight=c_weight) loss = loss_c * self.loss_config['coarse_weight'] loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()}) # 2. fine-level loss loss_f= self.compute_fine_loss(data['expec_f'], data['expec_f_gt']) loss_f_2 = self._compute_fine_loss_t_s_kld(data['expec_f'], data['expec_f_gt'], data['expec_f_t']) if loss_f is not None and loss_f_2 is not None: loss_f =loss_f*0.5+0.5*loss_f_2 loss += loss_f * self.loss_config['fine_weight'] loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()}) else: assert self.training is False loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound loss_scalars.update({'loss': loss.clone().detach().cpu()}) data.update({"loss": loss, "loss_scalars": loss_scalars})

py

tencent-ml-images

tencent-ml-images-master/models/resnet.py

"""ResNet model Related papers: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 [2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys sys.path.insert(0, '../') from flags import FLAGS import tensorflow as tf class ResNet(object): def __init__(self, images, is_training): """Net constructor Args: images: 4-D Tensor of images with Shape [batch_size, image_size, image_size, 3] is_training: bool, used in batch normalization Return: A wrapper For building model """ self.is_training = is_training self.filters = [256, 512, 1024, 2048] # feature map size for each stages self.strides = [2, 2, 2, 2] # conv strides for each stages's first block if FLAGS.resnet_size == 50: # resnet size paramters self.stages = [3, 4, 6, 3] elif FLAGS.resnet_size == 101: self.stages = [3, 4, 23, 3] elif FLAGS.resnet_size == 152: self.stages = [3, 8, 36, 3] else: raise ValueError('resnet_size %d Not implement:' % FLAGS.resnet_size) self.data_format = FLAGS.data_format self.num_classes = FLAGS.class_num self.images = images if self.data_format == "NCHW": self.images = tf.transpose(images, [0, 3, 1, 2]) def build_model(self): # Initial net with tf.variable_scope('init'): x = self.images x = self._pre_padding_conv('init_conv', x, 7, 64, 2) # 4 stages for i in range(0, len(self.stages)): with tf.variable_scope('stages_%d_block_%d' % (i,0)): x = self._bottleneck_residual( x, self.filters[i], self.strides[i], 'conv', self.is_training) for j in range(1, self.stages[i]): with tf.variable_scope('stages_%d_block_%d' % (i,j)): x = self._bottleneck_residual( x, self.filters[i], 1, 'identity', self.is_training) # class wise avg pool with tf.variable_scope('global_pool'): x = self._batch_norm('bn', x, self.is_training) x = self._relu(x) x = self._global_avg_pool(x) # extract features self.feat=x # logits with tf.variable_scope("logits"): self.logit = self._fully_connected(x, out_dim=self.num_classes) return self.logit def _bottleneck_residual(self, x, out_channel, strides, _type, is_training): """Residual Block Args: x : A 4-D tensor out_channels : out feature map size of residual block strides : conv strides of block _type: short cut type, 'conv' or 'identity' is_training : A Boolean for whether the model is in training or inference mdoel """ # short cut orig_x = x if _type=='conv': orig_x = self._batch_norm('conv1_b1_bn', orig_x, is_training) orig_x = self._relu(orig_x) orig_x = self._pre_padding_conv('conv1_b1', orig_x, 1, out_channel, strides) # bottleneck_residual_block x = self._batch_norm('conv1_b2_bn', x, is_training) x = self._relu(x) x = self._pre_padding_conv('conv1_b2', x, 1, out_channel/4, 1) x = self._batch_norm('conv2_b2_bn', x, is_training) x = self._relu(x) x = self._pre_padding_conv('conv2_b2', x, 3, out_channel/4, strides) x = self._batch_norm('conv3_b2_bn', x, is_training) x = self._relu(x) x = self._pre_padding_conv('conv3_b2', x, 1, out_channel, 1) # sum return x + orig_x def _batch_norm(self, name, x, is_training=True): """Batch normalization. Considering the performance, we use batch_normalization in contrib/layers/python/layers/layers.py instead of tf.nn.batch_normalization and set fused=True Args: x: input tensor is_training: Whether to return the output in training mode or in inference mode, use the argment in finetune """ with tf.variable_scope(name): return tf.layers.batch_normalization( inputs=x, axis=1 if self.data_format == 'NCHW' else 3, momentum = FLAGS.batch_norm_decay, epsilon = FLAGS.batch_norm_epsilon, center=True, scale=True, training=is_training, fused=True ) def _pre_padding(self, x, kernel_size): """Padding Based On Kernel_size""" pad_total = kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg if self.data_format == 'NCHW': x = tf.pad(x, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]]) else: x = tf.pad(x, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) return x def _pre_padding_conv(self, name, x, kernel_size, out_channels, strides, bias=False): """Convolution As the way of padding in conv is depended on input size and kernel size, which is very different with caffe So we will do pre-padding to Align the padding operation. Args: x : A 4-D tensor kernel_size : size of kernel, here we just use square conv kernel out_channels : out feature map size strides : conv stride bias : bias may always be false """ if strides > 1: x = self._pre_padding(x, kernel_size) with tf.variable_scope(name): return tf.layers.conv2d( inputs = x, filters = out_channels, kernel_size=kernel_size, strides=strides, padding=('SAME' if strides == 1 else 'VALID'), use_bias=bias, kernel_initializer=tf.variance_scaling_initializer(), data_format= 'channels_first' if self.data_format == 'NCHW' else 'channels_last') def _relu(self, x, leakiness=0.0): """ Relu. With optical leakiness support Note: if leakiness set zero, we will use tf.nn.relu for concern about performance Args: x : A 4-D tensor leakiness : slope when x < 0 """ if leakiness==0.0: return tf.nn.relu(x) else: return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu') def _global_avg_pool(self, x): """ Global Average Pool, for concern about performance we use tf.reduce_mean instead of tf.layers.average_pooling2d Args: x: 4-D Tensor """ assert x.get_shape().ndims == 4 axes = [2, 3] if self.data_format == 'NCHW' else [1, 2] return tf.reduce_mean(x, axes, keep_dims=True) def _fully_connected(self, x, out_dim): """ As tf.layers.dense need 2-D tensor, reshape it first Args: x : 4-D Tensor out_dim : dimensionality of the output space. """ assert x.get_shape().ndims == 4 axes = 1 if self.data_format == 'NCHW' else -1 x = tf.reshape(x, shape=[-1, x.get_shape()[axes]]) return tf.layers.dense(x, units = out_dim)

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/main.py

import sys import argparse import os import random import numpy import pandas as pd import torch import torch.backends.cudnn as cudnn import torch.nn.parallel import torch.optim import torch.utils.data import agents import utils numpy.set_printoptions(edgeitems=5, linewidth=160, formatter={'float': '{:0.6f}'.format}) torch.set_printoptions(edgeitems=5, precision=6, linewidth=160) pd.options.display.float_format = '{:,.6f}'.format pd.set_option('display.width', 160) parser = argparse.ArgumentParser(description='Main') parser.add_argument('-x', '--executions', default=1, type=int, metavar='N', help='Number of executions (default: 1)') parser.add_argument('-w', '--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)') parser.add_argument('-e', '--epochs', default=300, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('-bs', '--batch-size', default=64, type=int, metavar='N', help='mini-batch size (default: 64)') parser.add_argument('-lr', '--original-learning-rate', default=0.1, type=float, metavar='LR', help='initial learning rate') parser.add_argument('-lrdr', '--learning-rate-decay-rate', default=0.1, type=float, metavar='LRDR', help='learning rate decay rate') parser.add_argument('-lrde', '--learning-rate-decay-epochs', default="150 200 250", metavar='LRDE', help='learning rate decay epochs') parser.add_argument('-lrdp', '--learning-rate-decay-period', default=500, type=int, metavar='LRDP', help='learning rate decay period') parser.add_argument('-mm', '--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('-wd', '--weight-decay', default=1*1e-4, type=float, metavar='W', help='weight decay (default: 1*1e-4)') parser.add_argument('-pf', '--print-freq', default=1, type=int, metavar='N', help='print frequency (default: 1)') parser.add_argument('-gpu', '--gpu-id', default='0', type=int, help='id for CUDA_VISIBLE_DEVICES') parser.add_argument('-ei', '--exps-inputs', default="", type=str, metavar='PATHS', help='Inputs paths for the experiments') parser.add_argument('-et', '--exps-types', default="", type=str, metavar='EXPERIMENTS', help='Experiments types to be performed') parser.add_argument('-ec', '--exps-configs', default="", type=str, metavar='CONFIGS', help='Experiments configs to be used') parser.add_argument('-sd', '--seed', default=42, type=int, metavar='N', help='Seed (default: 42)') args = parser.parse_args() args.exps_inputs = args.exps_inputs.split(":") args.exps_types = args.exps_types.split(":") args.exps_configs = args.exps_configs.split(":") args.learning_rate_decay_epochs = [int(item) for item in args.learning_rate_decay_epochs.split()] random.seed(args.seed) numpy.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) print("seed", args.seed) cudnn.benchmark = False if args.executions == 1: cudnn.deterministic = True print("Deterministic!!!") else: cudnn.deterministic = False print("No deterministic!!!") torch.cuda.set_device(args.gpu_id) print('\n__Python VERSION:', sys.version) print('__pyTorch VERSION:', torch.__version__) print('__Number CUDA Devices:', torch.cuda.device_count()) print('Active CUDA Device: GPU', torch.cuda.current_device()) def main(): print("\n\n\n\n\n\n") print("***************************************************************") print("***************************************************************") print("***************************************************************") print("***************************************************************") for args.exp_input in args.exps_inputs: for args.exp_type in args.exps_types: for args.exp_config in args.exps_configs: print("\n\n\n\n") print("***************************************************************") print("EXPERIMENT INPUT:", args.exp_input) print("EXPERIMENT TYPE:", args.exp_type) print("EXPERIMENT CONFIG:", args.exp_config) args.experiment_path = os.path.join("experiments", args.exp_input, args.exp_type, args.exp_config) if not os.path.exists(args.experiment_path): os.makedirs(args.experiment_path) print("EXPERIMENT PATH:", args.experiment_path) args.executions_best_results_file_path = os.path.join(args.experiment_path, "results_best.csv") args.executions_raw_results_file_path = os.path.join(args.experiment_path, "results_raw.csv") for config in args.exp_config.split("+"): config = config.split("~") if config[0] == "data": args.dataset = str(config[1]) print("DATASET:", args.dataset) elif config[0] == "model": args.model_name = str(config[1]) print("MODEL:", args.model_name) elif config[0] == "loss": args.loss = str(config[1]) print("LOSS:", args.loss) args.number_of_model_classes = None if args.dataset == "cifar10": args.number_of_model_classes = args.number_of_model_classes if args.number_of_model_classes else 10 elif args.dataset == "cifar100": args.number_of_model_classes = args.number_of_model_classes if args.number_of_model_classes else 100 elif args.dataset == "svhn": args.number_of_model_classes = args.number_of_model_classes if args.number_of_model_classes else 10 print("***************************************************************") for args.execution in range(1, args.executions + 1): print("\n\n################ EXECUTION:", args.execution, "OF", args.executions, "################") args.best_model_file_path = os.path.join(args.experiment_path, "model" + str(args.execution) + ".pth") utils.save_dict_list_to_csv([vars(args)], args.experiment_path, args.exp_type+"_args") print("\nARGUMENTS:", dict(utils.load_dict_list_from_csv(args.experiment_path, args.exp_type+"_args")[0])) cnn_agent = agents.ClassifierAgent(args) cnn_agent.train_classify() experiment_results = pd.read_csv(os.path.join(os.path.join(args.experiment_path, "results_best.csv"))) print("\n################################\n", "EXPERIMENT RESULTS", "\n################################") print(args.experiment_path) print("\n", experiment_results.transpose()) if __name__ == '__main__': main()

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/example.py

# Code reused from: https://github.com/kuangliu/pytorch-cifar import torch import torch.backends.cudnn as cudnn import torchvision import torchvision.transforms as transforms import os import net import losses import tools from torchmetrics import AUROC import random import numpy import torchnet as tnt base_seed = 42 random.seed(base_seed) numpy.random.seed(base_seed) torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) cudnn.benchmark = False cudnn.deterministic = True device = 'cuda' if torch.cuda.is_available() else 'cpu' best_acc = 0 # best test accuracy start_epoch = 1 # start from epoch one # Data print('==> Preparing data...') transform_train = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261)),]) transform_test = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261)),]) trainset = torchvision.datasets.CIFAR10(root='data/cifar10', train=True, download=True, transform=transform_train) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=4, worker_init_fn=lambda worker_id: random.seed(base_seed + worker_id)) testset = torchvision.datasets.CIFAR10(root='data/cifar10', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False, num_workers=4) # Model print('==> Building model...') model = net.DenseNet3(100, 10) model = model.to(device) ############################################# #criterion = nn.CrossEntropyLoss() criterion = losses.IsoMaxPlusLossSecondPart() ############################################# optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=1*1e-4) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[150, 200, 250], gamma=0.1) def train(epoch): print('Epoch: %d' % epoch) model.train() train_loss = 0 correct = 0 total = 0 for batch_idx, (inputs, targets) in enumerate(trainloader): inputs, targets = inputs.to(device), targets.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() tools.progress_bar(batch_idx, len(trainloader), 'Loss: %.4f | Acc: %.4f%% (%d/%d)' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total)) def test(epoch): global best_acc model.eval() test_loss = 0 correct = 0 total = 0 with torch.no_grad(): for batch_idx, (inputs, targets) in enumerate(testloader): inputs, targets = inputs.to(device), targets.to(device) outputs = model(inputs) loss = criterion(outputs, targets) test_loss += loss.item() _, predicted = outputs.max(1) total += targets.size(0) correct += predicted.eq(targets).sum().item() tools.progress_bar(batch_idx, len(testloader), 'Loss: %.4f | Acc: %.4f%% (%d/%d)' % (test_loss/(batch_idx+1), 100.*correct/total, correct, total)) # Save checkpoint acc = 100.*correct/total if acc > best_acc: print('Saving...') state = { 'model': model.state_dict(), 'acc': acc, 'epoch': epoch,} if not os.path.isdir('checkpoint'): os.mkdir('checkpoint') torch.save(state, 'checkpoint/ckpt.pth') best_acc = acc def detect(inloader, oodloader): auroc = AUROC(pos_label=1) auroctnt = tnt.meter.AUCMeter() model.eval() with torch.no_grad(): for _, (inputs, targets) in enumerate(inloader): inputs, targets = inputs.to(device), targets.to(device) targets.fill_(1) outputs = model(inputs) #probabilities = torch.nn.Softmax(dim=1)(outputs) #score = probabilities.max(dim=1)[0] # this is the maximum probability score #entropies = -(probabilities * torch.log(probabilities)).sum(dim=1) #score = -entropies # this is the negative entropy score # the negative entropy score is the best option for the IsoMax loss # outputs are equal to logits, which in turn are equivalent to negative distances score = outputs.max(dim=1)[0] # this is the minimum distance score # the minimum distance score is the best option for the IsoMax+ loss auroc.update(score, targets) auroctnt.add(score, targets) for _, (inputs, targets) in enumerate(oodloader): inputs, targets = inputs.to(device), targets.to(device) targets.fill_(0) outputs = model(inputs) #probabilities = torch.nn.Softmax(dim=1)(outputs) #score = probabilities.max(dim=1)[0] # this is the maximum probability score #entropies = -(probabilities * torch.log(probabilities)).sum(dim=1) #score = -entropies # this is the negative entropy score # the negative entropy score is the best option for the IsoMax loss # outputs are equal to logits, which in turn are equivalent to negative distances score = outputs.max(dim=1)[0] # this is the minimum distance score for detection # the minimum distance score is the best option for the IsoMax+ loss auroc.update(score, targets) auroctnt.add(score, targets) return auroc.compute(), auroctnt.value()[0] total_epochs = 300 for epoch in range(start_epoch, start_epoch + total_epochs): print() for param_group in optimizer.param_groups: print("LEARNING RATE: ", param_group["lr"]) train(epoch) test(epoch) scheduler.step() checkpoint = torch.load('checkpoint/ckpt.pth') model.load_state_dict(checkpoint['model']) test_acc = checkpoint['acc'] print() print("###################################################") print("Test Accuracy (%): {0:.4f}".format(test_acc)) print("###################################################") print() dataroot = os.path.expanduser(os.path.join('data', 'Imagenet_resize')) oodset = torchvision.datasets.ImageFolder(dataroot, transform=transform_test) oodloader = torch.utils.data.DataLoader(oodset, batch_size=64, shuffle=False, num_workers=4) auroc = detect(testloader, oodloader) print() print("#################################################################################################################") print("Detection performance for ImageNet Resize as Out-of-Distribution [AUROC] (%): {0:.4f}".format(100. * auroc[0].item()), auroc[1]) print("#################################################################################################################") print() dataroot = os.path.expanduser(os.path.join('data', 'LSUN_resize')) oodset = torchvision.datasets.ImageFolder(dataroot, transform=transform_test) oodloader = torch.utils.data.DataLoader(oodset, batch_size=64, shuffle=False, num_workers=4) auroc = detect(testloader, oodloader) print() print("#################################################################################################################") print("Detection performance for LSUN Resize as Out-of-Distribution [AUROC] (%): {0:.4f}".format(100. * auroc[0].item()), auroc[1]) print("#################################################################################################################") print() oodset = torchvision.datasets.SVHN(root='data/svhn', split="test", download=True, transform=transform_test) oodloader = torch.utils.data.DataLoader(oodset, batch_size=64, shuffle=False, num_workers=4) auroc = detect(testloader, oodloader) print() print("#################################################################################################################") print("Detection performance for SVHN as Out-of-Distribution [AUROC] (%): {0:.4f}".format(100. * auroc[0].item()), auroc[1]) print("#################################################################################################################") print()

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/detect.py

from __future__ import print_function import argparse import torch import models import os import losses import data_loader import calculate_log as callog from torchvision import transforms parser = argparse.ArgumentParser(description='PyTorch code: OOD detector') parser.add_argument('--batch_size', type=int, default=64, metavar='N', help='batch size for data loader') parser.add_argument('--dataset', required=True, help='cifar10 | cifar100 | svhn') parser.add_argument('--dataroot', default='data', help='path to dataset') parser.add_argument('--net_type', required=True, help='resnet | densenet') parser.add_argument('--gpu', type=int, default=0, help='gpu index') parser.add_argument('--loss', required=True, help='the loss used') parser.add_argument('--dir', default="", type=str, help='Part of the dir to use') parser.add_argument('-x', '--executions', default=1, type=int, metavar='N', help='Number of executions (default: 1)') args = parser.parse_args() print(args) torch.cuda.set_device(args.gpu) def main(): dir_path = os.path.join("experiments", args.dir, "train_classify", "data~"+args.dataset+"+model~"+args.net_type+"+loss~"+str(args.loss)) file_path = os.path.join(dir_path, "results_odd.csv") with open(file_path, "w") as results_file: results_file.write( "EXECUTION,MODEL,IN-DATA,OUT-DATA,LOSS,AD-HOC,SCORE,INFER-LEARN,INFER-TRANS," "TNR,AUROC,DTACC,AUIN,AUOUT,CPU_FALSE,CPU_TRUE,GPU_FALSE,GPU_TRUE,TEMPERATURE,MAGNITUDE\n") args_outf = os.path.join("temporary", args.dir, args.loss, args.net_type + '+' + args.dataset) if os.path.isdir(args_outf) == False: os.makedirs(args_outf) # define number of classes if args.dataset == 'cifar100': args.num_classes = 100 elif args.dataset == 'imagenet32': args.num_classes = 1000 else: args.num_classes = 10 if args.dataset == 'cifar10': out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize'] elif args.dataset == 'cifar100': out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize'] elif args.dataset == 'svhn': out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize'] if args.dataset == 'cifar10': in_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))]) elif args.dataset == 'cifar100': in_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.507, 0.486, 0.440), (0.267, 0.256, 0.276))]) elif args.dataset == 'svhn': in_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.437, 0.443, 0.472), (0.198, 0.201, 0.197))]) for args.execution in range(1, args.executions + 1): print("EXECUTION:", args.execution) pre_trained_net = os.path.join(dir_path, "model" + str(args.execution) + ".pth") if args.loss.split("_")[0] == "softmax": loss_first_part = losses.SoftMaxLossFirstPart scores = ["ES"] elif args.loss.split("_")[0] == "isomax": loss_first_part = losses.IsoMaxLossFirstPart scores = ["ES"] elif args.loss.split("_")[0] == "isomaxplus": loss_first_part = losses.IsoMaxPlusLossFirstPart scores = ["MDS"] # load networks if args.net_type == 'densenetbc100': model = models.DenseNet3(100, int(args.num_classes), loss_first_part=loss_first_part) elif args.net_type == 'resnet110': model = models.ResNet110(num_c=args.num_classes, loss_first_part=loss_first_part) model.load_state_dict(torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu))) model.cuda() print('load model: ' + args.net_type) # load dataset print('load target valid data: ', args.dataset) _, test_loader = data_loader.getTargetDataSet(args.dataset, args.batch_size, in_transform, args.dataroot) for score in scores: print("\n\n\n###############################") print("###############################") print("SCORE:", score) print("###############################") print("###############################") base_line_list = [] get_scores(model, test_loader, args_outf, True, score) out_count = 0 for out_dist in out_dist_list: out_test_loader = data_loader.getNonTargetDataSet(out_dist, args.batch_size, in_transform, args.dataroot) print('Out-distribution: ' + out_dist) get_scores(model, out_test_loader, args_outf, False, score) test_results = callog.metric(args_outf, ['PoT']) base_line_list.append(test_results) out_count += 1 # print the results mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT'] print('Baseline method: train in_distribution: ' + args.dataset + '==========') count_out = 0 for results in base_line_list: print('out_distribution: '+ out_dist_list[count_out]) for mtype in mtypes: print(' {mtype:6s}'.format(mtype=mtype), end='') print('\n{val:6.2f}'.format(val=100.*results['PoT']['TNR']), end='') print(' {val:6.2f}'.format(val=100.*results['PoT']['AUROC']), end='') print(' {val:6.2f}'.format(val=100.*results['PoT']['DTACC']), end='') print(' {val:6.2f}'.format(val=100.*results['PoT']['AUIN']), end='') print(' {val:6.2f}\n'.format(val=100.*results['PoT']['AUOUT']), end='') print('') #Saving odd results: with open(file_path, "a") as results_file: results_file.write("{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\n".format( str(args.execution), args.net_type, args.dataset, out_dist_list[count_out], str(args.loss), "NATIVE", score, 'NO', False, '{:.2f}'.format(100.*results['PoT']['TNR']), '{:.2f}'.format(100.*results['PoT']['AUROC']), '{:.2f}'.format(100.*results['PoT']['DTACC']), '{:.2f}'.format(100.*results['PoT']['AUIN']), '{:.2f}'.format(100.*results['PoT']['AUOUT']), 0, 0, 0, 0, 1, 0)) count_out += 1 def get_scores(model, test_loader, outf, out_flag, score_type=None): model.eval() total = 0 if out_flag == True: temp_file_name_val = '%s/confidence_PoV_In.txt'%(outf) temp_file_name_test = '%s/confidence_PoT_In.txt'%(outf) else: temp_file_name_val = '%s/confidence_PoV_Out.txt'%(outf) temp_file_name_test = '%s/confidence_PoT_Out.txt'%(outf) g = open(temp_file_name_val, 'w') f = open(temp_file_name_test, 'w') for data, _ in test_loader: total += data.size(0) data = data.cuda() with torch.no_grad(): logits = model(data) probabilities = torch.nn.Softmax(dim=1)(logits) if score_type == "MPS": # the maximum probability score soft_out = probabilities.max(dim=1)[0] elif score_type == "ES": # the negative entropy score soft_out = (probabilities * torch.log(probabilities)).sum(dim=1) elif score_type == "MDS": # the minimum distance score soft_out = logits.max(dim=1)[0] for i in range(data.size(0)): f.write("{}\n".format(soft_out[i])) f.close() g.close() if __name__ == '__main__': main()

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/data_loader.py

import torch from torchvision import datasets import os def getSVHN(batch_size, TF, data_root='data', train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'svhn')) kwargs.pop('input_size', None) ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.SVHN(root=data_root, split='train', download=True, transform=TF), batch_size=batch_size, shuffle=True, **kwargs) ds.append(train_loader) if val: test_loader = torch.utils.data.DataLoader( datasets.SVHN(root=data_root, split='test', download=True, transform=TF,), batch_size=batch_size, shuffle=False, **kwargs) ds.append(test_loader) ds = ds[0] if len(ds) == 1 else ds return ds def getCIFAR10(batch_size, TF, data_root='data', train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'cifar10')) kwargs.pop('input_size', None) ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.CIFAR10(root=data_root, train=True, download=True, transform=TF), batch_size=batch_size, shuffle=True, **kwargs) ds.append(train_loader) if val: test_loader = torch.utils.data.DataLoader( datasets.CIFAR10(root=data_root, train=False, download=True, transform=TF), batch_size=batch_size, shuffle=False, **kwargs) ds.append(test_loader) ds = ds[0] if len(ds) == 1 else ds return ds def getCIFAR100(batch_size, TF, data_root='data', TTF=None, train=True, val=True, **kwargs): data_root = os.path.expanduser(os.path.join(data_root, 'cifar100')) kwargs.pop('input_size', None) ds = [] if train: train_loader = torch.utils.data.DataLoader( datasets.CIFAR100(root=data_root, train=True, download=True, transform=TF, target_transform=TTF), batch_size=batch_size, shuffle=True, **kwargs) ds.append(train_loader) if val: test_loader = torch.utils.data.DataLoader( datasets.CIFAR100(root=data_root, train=False, download=True, transform=TF, target_transform=TTF), batch_size=batch_size, shuffle=False, **kwargs) ds.append(test_loader) ds = ds[0] if len(ds) == 1 else ds return ds def getTargetDataSet(data_type, batch_size, input_TF, dataroot): if data_type == 'cifar10': train_loader, test_loader = getCIFAR10(batch_size=batch_size, TF=input_TF, data_root=dataroot, num_workers=1) elif data_type == 'cifar100': train_loader, test_loader = getCIFAR100(batch_size=batch_size, TF=input_TF, data_root=dataroot, TTF=None, num_workers=1) elif data_type == 'svhn': train_loader, test_loader = getSVHN(batch_size=batch_size, TF=input_TF, data_root=dataroot, num_workers=1) return train_loader, test_loader def getNonTargetDataSet(data_type, batch_size, input_TF, dataroot): if data_type == 'cifar10': _, test_loader = getCIFAR10(batch_size=batch_size, TF=input_TF, data_root=dataroot, num_workers=1) elif data_type == 'svhn': _, test_loader = getSVHN(batch_size=batch_size, TF=input_TF, data_root=dataroot, num_workers=1) elif data_type == 'cifar100': _, test_loader = getCIFAR100(batch_size=batch_size, TF=input_TF, data_root=dataroot, TTF=lambda x: 0, num_workers=1) elif data_type == 'imagenet_resize': dataroot = os.path.expanduser(os.path.join(dataroot, 'Imagenet_resize')) testsetout = datasets.ImageFolder(dataroot, transform=input_TF) test_loader = torch.utils.data.DataLoader(testsetout, batch_size=batch_size, shuffle=False, num_workers=1) elif data_type == 'lsun_resize': dataroot = os.path.expanduser(os.path.join(dataroot, 'LSUN_resize')) testsetout = datasets.ImageFolder(dataroot, transform=input_TF) test_loader = torch.utils.data.DataLoader(testsetout, batch_size=batch_size, shuffle=False, num_workers=1) return test_loader

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/tools.py

# Code reused from: https://github.com/kuangliu/pytorch-cifar '''Some helper functions for PyTorch, including: - get_mean_and_std: calculate the mean and std value of dataset. - msr_init: net parameter initialization. - progress_bar: progress bar mimic xlua.progress. ''' import os import sys import time _, term_width = os.popen('stty size', 'r').read().split() term_width = int(term_width) TOTAL_BAR_LENGTH = 65. last_time = time.time() begin_time = last_time def progress_bar(current, total, msg=None): global last_time, begin_time if current == 0: begin_time = time.time() # Reset for new bar. cur_len = int(TOTAL_BAR_LENGTH*current/total) rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 sys.stdout.write(' [') for i in range(cur_len): sys.stdout.write('=') sys.stdout.write('>') for i in range(rest_len): sys.stdout.write('.') sys.stdout.write(']') cur_time = time.time() step_time = cur_time - last_time last_time = cur_time tot_time = cur_time - begin_time L = [] L.append(' Step: %s' % format_time(step_time)) L.append(' | Tot: %s' % format_time(tot_time)) if msg: L.append(' | ' + msg) msg = ''.join(L) sys.stdout.write(msg) for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3): sys.stdout.write(' ') # Go back to the center of the bar. for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2): sys.stdout.write('\b') sys.stdout.write(' %d/%d ' % (current+1, total)) if current < total-1: sys.stdout.write('\r') else: sys.stdout.write('\n') sys.stdout.flush() def format_time(seconds): days = int(seconds / 3600/24) seconds = seconds - days*3600*24 hours = int(seconds / 3600) seconds = seconds - hours*3600 minutes = int(seconds / 60) seconds = seconds - minutes*60 secondsf = int(seconds) seconds = seconds - secondsf millis = int(seconds*1000) f = '' i = 1 if days > 0: f += str(days) + 'D' i += 1 if hours > 0 and i <= 2: f += str(hours) + 'h' i += 1 if minutes > 0 and i <= 2: f += str(minutes) + 'm' i += 1 if secondsf > 0 and i <= 2: f += str(secondsf) + 's' i += 1 if millis > 0 and i <= 2: f += str(millis) + 'ms' i += 1 if f == '': f = '0ms' return f

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/net.py

# Code reused from: https://github.com/kuangliu/pytorch-cifar import math import torch import torch.nn as nn import torch.nn.functional as F import losses class BasicBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(BasicBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, training=self.training) return torch.cat([x, out], 1) class BottleneckBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(BottleneckBlock, self).__init__() inter_planes = out_planes * 4 self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, inter_planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(inter_planes) self.conv2 = nn.Conv2d(inter_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) out = self.conv2(self.relu(self.bn2(out))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) return torch.cat([x, out], 1) class TransitionBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(TransitionBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) return F.avg_pool2d(out, 2) class DenseBlock(nn.Module): def __init__(self, nb_layers, in_planes, growth_rate, block, dropRate=0.0): super(DenseBlock, self).__init__() self.layer = self._make_layer(block, in_planes, growth_rate, nb_layers, dropRate) def _make_layer(self, block, in_planes, growth_rate, nb_layers, dropRate): layers = [] for i in range(int(nb_layers)): layers.append(block(in_planes + i * growth_rate, growth_rate, dropRate)) return nn.Sequential(*layers) def forward(self, x): return self.layer(x) class DenseNet3(nn.Module): def __init__(self, depth, num_classes, growth_rate=12, reduction=0.5, bottleneck=True, dropRate=0.0): super(DenseNet3, self).__init__() in_planes = 2 * growth_rate n = (depth - 4) / 3 if bottleneck == True: n = n / 2 block = BottleneckBlock else: block = BasicBlock # 1st conv before any dense block self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1, padding=1, bias=False) # 1st block self.block1 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) self.trans1 = TransitionBlock(in_planes, int(math.floor(in_planes * reduction)), dropRate=dropRate) in_planes = int(math.floor(in_planes * reduction)) # 2nd block self.block2 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) self.trans2 = TransitionBlock(in_planes, int(math.floor(in_planes * reduction)), dropRate=dropRate) in_planes = int(math.floor(in_planes * reduction)) # 3rd block self.block3 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) # global average pooling and classifier self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.in_planes = in_planes ######################################################################## #self.classifier = nn.Linear(in_planes, num_classes) self.classifier = losses.IsoMaxPlusLossFirstPart(in_planes, num_classes) ######################################################################## # Official init from torch repo. for m in self.modules(): if isinstance(m, nn.Conv2d): #nn.init.kaiming_normal_(m.weight) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) #elif isinstance(m, nn.Linear): # nn.init.constant_(m.bias, 0) def forward(self, x): out = self.conv1(x) out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, 8) out = out.view(-1, self.in_planes) return self.classifier(out)

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/analyze.py

import argparse import os import torch import sys import numpy as np import pandas as pd import random pd.options.display.float_format = '{:,.4f}'.format pd.set_option('display.width', 160) parser = argparse.ArgumentParser(description='Analize results in csv files') parser.add_argument('-p', '--path', default="", type=str, help='Path for the experiments to be analized') parser.set_defaults(argument=True) random.seed(1234) np.random.seed(1234) torch.manual_seed(1234) torch.cuda.manual_seed(1234) def main(): DATASETS = ['svhn', 'cifar10', 'cifar100'] MODELS = ['densenetbc100', 'resnet110'] LOSSES = ['softmax_no_no_no_final', 'isomax_no_no_no_final', 'isomaxplus_no_no_no_final', ] print(DATASETS) print(MODELS) print(LOSSES) args = parser.parse_args() path = os.path.join("experiments", args.path) if not os.path.exists(path): sys.exit('You should pass a valid path to analyze!!!') print("\n#####################################") print("########## FINDING FILES ############") print("#####################################") list_of_files = [] file_names_dict_of_lists = {} for (dir_path, dir_names, file_names) in os.walk(path): for filename in file_names: if filename.endswith('.csv') or filename.endswith('.npy') or filename.endswith('.pth'): if filename not in file_names_dict_of_lists: file_names_dict_of_lists[filename] = [os.path.join(dir_path, filename)] else: file_names_dict_of_lists[filename] += [os.path.join(dir_path, filename)] list_of_files += [os.path.join(dir_path, filename)] print() for key in file_names_dict_of_lists: print(key) #print(file_names_dict_of_lists[key]) print("\n#####################################") print("######## TABLE: RAW RESULTS #########") print("#####################################") data_frame_list = [] for file in file_names_dict_of_lists['results_raw.csv']: data_frame_list.append(pd.read_csv(file)) raw_results_data_frame = pd.concat(data_frame_list) print(raw_results_data_frame[:30]) print("\n#####################################") print("###### TABLE: BEST ACCURACIES #######") print("#####################################") data_frame_list = [] for file in file_names_dict_of_lists['results_best.csv']: data_frame_list.append(pd.read_csv(file)) best_results_data_frame = pd.concat(data_frame_list) best_results_data_frame.to_csv(os.path.join(path, 'all_results_best.csv'), index=False) for data in DATASETS: for model in MODELS: print("\n########") print(data) print(model) df = best_results_data_frame.loc[ best_results_data_frame['DATA'].isin([data]) & best_results_data_frame['MODEL'].isin([model]) ] df = df.rename(columns={'VALID MAX_PROBS MEAN': 'MAX_PROBS', 'VALID ENTROPIES MEAN': 'ENTROPIES', 'VALID INTRA_LOGITS MEAN': 'INTRA_LOGITS', 'VALID INTER_LOGITS MEAN': 'INTER_LOGITS'}) df = df.groupby(['LOSS'], as_index=False)[[ 'TRAIN LOSS', 'TRAIN ACC1','VALID LOSS', 'VALID ACC1', 'ENTROPIES', ]].agg(['mean','std','count']) df = df.sort_values([('VALID ACC1','mean')], ascending=False) print(df) print("########\n") print("\n#####################################") print("######## TABLE: ODD METRICS #########") print("#####################################") data_frame_list = [] for file in file_names_dict_of_lists['results_odd.csv']: data_frame_list.append(pd.read_csv(file)) best_results_data_frame = pd.concat(data_frame_list) best_results_data_frame.to_csv(os.path.join(path, 'all_results_odd.csv'), index=False) for data in DATASETS: for model in MODELS: print("\n#########################################################################################################") print("#########################################################################################################") print("#########################################################################################################") print("#########################################################################################################") print(data) print(model) df = best_results_data_frame.loc[ best_results_data_frame['IN-DATA'].isin([data]) & best_results_data_frame['MODEL'].isin([model]) & best_results_data_frame['SCORE'].isin(["MPS","ES","MDS"]) & best_results_data_frame['OUT-DATA'].isin(['svhn','lsun_resize','imagenet_resize','cifar10']) ] df = df[['MODEL','IN-DATA','LOSS','SCORE','EXECUTION','OUT-DATA','TNR','AUROC','DTACC','AUIN','AUOUT']] ndf = df.groupby(['LOSS','SCORE','OUT-DATA'], as_index=False)[['TNR','AUROC']].agg(['mean','std','count']) #print(ndf) #print() ndf = df.groupby(['LOSS','SCORE','OUT-DATA']).agg( mean_TNR=('TNR', 'mean'), std_TNR=('TNR', 'std'), count_TNR=('TNR', 'count'), mean_AUROC=('AUROC', 'mean'), std_AUROC=('AUROC', 'std'), count_AUROC=('AUROC', 'count')) #nndf = nndf.sort_values(['mean_AUROC'], ascending=False) #print(nndf) #print() nndf = ndf.groupby(['LOSS','SCORE']).agg( mean_mean_TNR=('mean_TNR', 'mean'), mean_std_TNR=('std_TNR', 'mean'), count_mean_TNR=('mean_TNR', 'count'), mean_mean_AUROC=('mean_AUROC', 'mean'), mean_std_AUROC=('std_AUROC', 'mean'), count_mean_AUROC=('mean_AUROC', 'count')) nndf = nndf.sort_values(['mean_mean_AUROC'], ascending=False) print(nndf) print() if __name__ == '__main__': main()

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/loaders/image.py

import random import torchvision import torchvision.transforms as transforms from torch.utils.data import DataLoader class ImageLoader: def __init__(self, args): self.args = args self.mnist = False if args.dataset == "cifar10": self.normalize = transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261)) self.train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), self.normalize,]) self.inference_transform = transforms.Compose([ transforms.ToTensor(), self.normalize,]) self.dataset_path = "data/cifar10" self.trainset_for_train = torchvision.datasets.CIFAR10( root=self.dataset_path, train=True, download=True, transform=self.train_transform) self.trainset_for_infer = torchvision.datasets.CIFAR10( root=self.dataset_path, train=True, download=True, transform=self.inference_transform) self.val_set = torchvision.datasets.CIFAR10( root=self.dataset_path, train=False, download=True, transform=self.inference_transform) self.outlier_data = None elif args.dataset == "cifar100": self.normalize = transforms.Normalize((0.507, 0.486, 0.440), (0.267, 0.256, 0.276)) self.train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), self.normalize,]) self.inference_transform = transforms.Compose([ transforms.ToTensor(), self.normalize,]) self.dataset_path = "data/cifar100" self.trainset_for_train = torchvision.datasets.CIFAR100( root=self.dataset_path, train=True, download=True, transform=self.train_transform) self.trainset_for_infer = torchvision.datasets.CIFAR100( root=self.dataset_path, train=True, download=True, transform=self.inference_transform) self.val_set = torchvision.datasets.CIFAR100( root=self.dataset_path, train=False, download=True, transform=self.inference_transform) self.outlier_data = None elif args.dataset == "svhn": self.normalize = transforms.Normalize((0.437, 0.443, 0.472), (0.198, 0.201, 0.197)) self.train_transform = transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), self.normalize,]) self.inference_transform = transforms.Compose([ transforms.ToTensor(), self.normalize,]) self.dataset_path = "data/svhn" self.trainset_for_train = torchvision.datasets.SVHN( root=self.dataset_path, split="train", download=True, transform=self.train_transform) self.trainset_for_infer = torchvision.datasets.SVHN( root=self.dataset_path, split="train", download=True, transform=self.inference_transform) self.val_set = torchvision.datasets.SVHN( root=self.dataset_path, split="test", download=True, transform=self.inference_transform) self.outlier_data = None def get_loaders(self): trainset_loader_for_train = DataLoader( self.trainset_for_train, batch_size=self.args.batch_size, shuffle=True, num_workers=self.args.workers, worker_init_fn=lambda worker_id: random.seed(self.args.seed + worker_id)) trainset_loader_for_infer = DataLoader(self.trainset_for_infer, batch_size=self.args.batch_size, shuffle=False, num_workers=self.args.workers,) valset_loader = DataLoader(self.val_set, batch_size=self.args.batch_size, shuffle=False, num_workers=self.args.workers) outlier_loader = DataLoader( self.outlier_data, batch_size=self.args.batch_size, shuffle=False, num_workers=self.args.workers, worker_init_fn=lambda worker_id: random.seed(self.args.seed + worker_id)) return trainset_loader_for_train, trainset_loader_for_infer, valset_loader, outlier_loader

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/models/densenet.py

# code reused from: https://github.com/kuangliu/pytorch-cifar import math import torch import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(BasicBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, training=self.training) return torch.cat([x, out], 1) class BottleneckBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(BottleneckBlock, self).__init__() inter_planes = out_planes * 4 self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, inter_planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(inter_planes) self.conv2 = nn.Conv2d(inter_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) out = self.conv2(self.relu(self.bn2(out))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) return torch.cat([x, out], 1) class TransitionBlock(nn.Module): def __init__(self, in_planes, out_planes, dropRate=0.0): super(TransitionBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) self.droprate = dropRate def forward(self, x): out = self.conv1(self.relu(self.bn1(x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, inplace=False, training=self.training) return F.avg_pool2d(out, 2) class DenseBlock(nn.Module): def __init__(self, nb_layers, in_planes, growth_rate, block, dropRate=0.0): super(DenseBlock, self).__init__() self.layer = self._make_layer(block, in_planes, growth_rate, nb_layers, dropRate) def _make_layer(self, block, in_planes, growth_rate, nb_layers, dropRate): layers = [] for i in range(int(nb_layers)): layers.append(block(in_planes + i * growth_rate, growth_rate, dropRate)) return nn.Sequential(*layers) def forward(self, x): return self.layer(x) class DenseNet3(nn.Module): def __init__(self, depth, num_classes, growth_rate=12, reduction=0.5, bottleneck=True, dropRate=0.0, loss_first_part=None): super(DenseNet3, self).__init__() in_planes = 2 * growth_rate n = (depth - 4) / 3 if bottleneck == True: n = n / 2 block = BottleneckBlock else: block = BasicBlock # 1st conv before any dense block self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1, padding=1, bias=False) # 1st block self.block1 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) self.trans1 = TransitionBlock(in_planes, int(math.floor(in_planes * reduction)), dropRate=dropRate) in_planes = int(math.floor(in_planes * reduction)) # 2nd block self.block2 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) self.trans2 = TransitionBlock(in_planes, int(math.floor(in_planes * reduction)), dropRate=dropRate) in_planes = int(math.floor(in_planes * reduction)) # 3rd block self.block3 = DenseBlock(n, in_planes, growth_rate, block, dropRate) in_planes = int(in_planes + n * growth_rate) # global average pooling and classifier self.bn1 = nn.BatchNorm2d(in_planes) self.relu = nn.ReLU(inplace=True) self.in_planes = in_planes ######################################################### #self.classifier = nn.Linear(in_planes, num_classes) self.classifier = loss_first_part(in_planes, num_classes) ######################################################### # Official init from torch repo. for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) #elif isinstance(m, nn.Linear): # nn.init.constant_(m.bias, 0) def forward(self, x): out = self.conv1(x) out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, out.size()[3]) out = out.view(-1, self.in_planes) return self.classifier(out) # function to extact the multiple features def feature_list(self, x): out_list = [] out = self.conv1(x) out_list.append(out) out = self.trans1(self.block1(out)) out_list.append(out) out = self.trans2(self.block2(out)) out_list.append(out) out = self.block3(out) out = self.relu(self.bn1(out)) out_list.append(out) out = F.avg_pool2d(out, out.size()[3]) out = out.view(-1, self.in_planes) return self.classifier(out), out_list def intermediate_forward(self, x, layer_index): out = self.conv1(x) if layer_index == 0: out = self.trans1(self.block1(out)) elif layer_index == 1: out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) elif layer_index == 2: out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) out = self.relu(self.bn1(out)) elif layer_index == 3: out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, out.size()[3]) return out # function to extact the penultimate features def penultimate_forward(self, x): out = self.conv1(x) out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) penultimate = self.relu(self.bn1(out)) out = F.avg_pool2d(penultimate, out.size()[3]) out = out.view(-1, self.in_planes) return self.classifier(out), penultimate def logits_features(self, x): out = self.conv1(x) out = self.trans1(self.block1(out)) out = self.trans2(self.block2(out)) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, out.size()[3]) features = out.view(-1, self.in_planes) logits = self.classifier(features) return logits, features

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/models/proper_resnet.py

# code reused from: https://github.com/akamaster/pytorch_resnet_cifar10 ''' Properly implemented ResNet-s for CIFAR10 as described in paper [1]. The implementation and structure of this file is hugely influenced by [2] which is implemented for ImageNet and doesn't have option A for identity. Moreover, most of the implementations on the web is copy-paste from torchvision's resnet and has wrong number of params. Proper ResNet-s for CIFAR10 (for fair comparision and etc.) has following number of layers and parameters: name | layers | params ResNet20 | 20 | 0.27M ResNet32 | 32 | 0.46M ResNet44 | 44 | 0.66M ResNet56 | 56 | 0.85M ResNet110 | 110 | 1.7M ResNet1202| 1202 | 19.4m which this implementation indeed has. Reference: [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385 [2] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py If you use this implementation in you work, please don't forget to mention the author, Yerlan Idelbayev. ''' import torch.nn as nn import torch.nn.functional as F class LambdaLayer(nn.Module): def __init__(self, lambd): super(LambdaLayer, self).__init__() self.lambd = lambd def forward(self, x): return self.lambd(x) class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1, option='A'): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != planes: if option == 'A': """ For CIFAR10 ResNet paper uses option A. """ self.shortcut = LambdaLayer(lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0)) elif option == 'B': self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes)) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=10, loss_first_part=None): super(ResNet, self).__init__() self.in_planes = 16 self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) ################ #self.linear = nn.Linear(64, num_classes) self.classifier = loss_first_part(64 * block.expansion, num_classes) ################ for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) #elif isinstance(m, nn.Linear): # nn.init.constant_(m.bias, 0) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1]*(num_blocks-1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = F.avg_pool2d(out, out.size()[3]) out = out.view(out.size(0), -1) out = self.classifier(out) return out def logits_features(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = F.avg_pool2d(out, out.size()[3]) features = out.view(out.size(0), -1) logits = self.classifier(features) return logits, features def feature_list(self, x): out_list = [] out = F.relu(self.bn1(self.conv1(x))) out_list.append(out) out = self.layer1(out) out_list.append(out) out = self.layer2(out) out_list.append(out) out = self.layer3(out) out_list.append(out) out = F.avg_pool2d(out, out.size()[3]) out_list.append(out) out = out.view(out.size(0), -1) out = self.classifier(out) return out, out_list def intermediate_forward(self, x, layer_index): out = F.relu(self.bn1(self.conv1(x))) if layer_index == 1: out = self.layer1(out) if layer_index == 2: out = self.layer1(out) out = self.layer2(out) if layer_index == 3: out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) if layer_index == 4: out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = F.avg_pool2d(out, out.size()[3]) return out def ResNet32(num_c, loss_first_part=None): return ResNet(BasicBlock, [5, 5, 5], num_classes=num_c, loss_first_part=loss_first_part) def ResNet56(num_c, loss_first_part=None): return ResNet(BasicBlock, [9, 9, 9], num_classes=num_c, loss_first_part=loss_first_part) def ResNet110(num_c, loss_first_part=None): return ResNet(BasicBlock, [18, 18, 18], num_classes=num_c, loss_first_part=loss_first_part)

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/agents/classifier.py

import os import sys import torch import models import loaders import losses import statistics import math import torchnet as tnt import numpy as np import utils class ClassifierAgent: def __init__(self, args): self.args = args self.epoch = None # create dataset image_loaders = loaders.ImageLoader(args) self.trainset_loader_for_train, self.trainset_loader_for_infer, self.valset_loader, self.outlier_loader = image_loaders.get_loaders() print("\nDATASET:", args.dataset) if self.args.loss.split("_")[0] == "softmax": loss_first_part = losses.SoftMaxLossFirstPart loss_second_part = losses.SoftMaxLossSecondPart elif self.args.loss.split("_")[0] == "isomax": loss_first_part = losses.IsoMaxLossFirstPart loss_second_part = losses.IsoMaxLossSecondPart elif self.args.loss.split("_")[0] == "isomaxplus": loss_first_part = losses.IsoMaxPlusLossFirstPart loss_second_part = losses.IsoMaxPlusLossSecondPart else: sys.exit('You should pass a valid loss to use!!!') # create model print("=> creating model '{}'".format(self.args.model_name)) if self.args.model_name == "densenetbc100": self.model = models.DenseNet3(100, int(self.args.number_of_model_classes), loss_first_part=loss_first_part) elif self.args.model_name == "resnet110": self.model = models.ResNet110(num_c=self.args.number_of_model_classes, loss_first_part=loss_first_part) self.model.cuda() # print and save model arch print("\nMODEL:", self.model) with open(os.path.join(self.args.experiment_path, 'model.arch'), 'w') as file: print(self.model, file=file) print("\n$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$") utils.print_num_params(self.model) print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n") # create loss self.criterion = loss_second_part() parameters = self.model.parameters() self.optimizer = torch.optim.SGD(parameters, lr=self.args.original_learning_rate,momentum=self.args.momentum, nesterov=True, weight_decay=args.weight_decay) self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=self.args.learning_rate_decay_epochs, gamma=args.learning_rate_decay_rate) print("\nTRAIN:", self.criterion, self.optimizer, self.scheduler) def train_classify(self): if self.args.execution == 1: with open(self.args.executions_best_results_file_path, "w") as best_results: best_results.write( "DATA,MODEL,LOSS,EXECUTION,EPOCH,TRAIN LOSS,TRAIN ACC1,TRAIN SCALE," "TRAIN INTRA_LOGITS MEAN,TRAIN INTRA_LOGITS STD,TRAIN INTER_LOGITS MEAN,TRAIN INTER_LOGITS STD," "TRAIN MAX_PROBS MEAN,TRAIN MAX_PROBS STD,TRAIN ENTROPIES MEAN,TRAIN ENTROPIES STD," "VALID LOSS,VALID ACC1,VALID SCALE," "VALID INTRA_LOGITS MEAN,VALID INTRA_LOGITS STD,VALID INTER_LOGITS MEAN,VALID INTER_LOGITS STD," "VALID MAX_PROBS MEAN,VALID MAX_PROBS STD,VALID ENTROPIES MEAN,VALID ENTROPIES STD\n") with open(self.args.executions_raw_results_file_path, "w") as raw_results: raw_results.write("DATA,MODEL,LOSS,EXECUTION,EPOCH,SET,METRIC,VALUE\n") print("\n################ TRAINING AND VALIDATING ################") best_model_results = {"VALID ACC1": 0} for self.epoch in range(1, self.args.epochs + 1): print("\n######## EPOCH:", self.epoch, "OF", self.args.epochs, "########") ###################################################################################################### ###################################################################################################### if self.epoch == 1: if self.args.loss.split("_")[0] == "softmax": # The IsoMax loss variants do not require warm-up!!! if self.args.model_name == 'resnet110' and self.args.dataset.startswith("cifar100"): print("Starting warm up training!!!\n" * 10) # for resnet110 original paper uses lr=0.01 for first 400 minibatches for warm-up # then switch back. In this setup it will correspond for first epoch. # Reference: # Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun # Deep Residual Learning for Image Recognition. arXiv:1512.03385 for param_group in self.optimizer.param_groups: param_group['lr'] = self.args.original_learning_rate*0.1 ###################################################################################################### ###################################################################################################### for param_group in self.optimizer.param_groups: print("\nLEARNING RATE:\t\t", param_group["lr"]) train_loss, train_acc1, train_scale, train_epoch_logits, train_epoch_metrics = self.train_epoch() ###################################################################################################### ###################################################################################################### if self.epoch == 1: if self.args.loss.split("_")[0] == "softmax": # The IsoMax loss variants do not require warm-up!!! if self.args.model_name == 'resnet110' and self.args.dataset.startswith("cifar100"): print("Finishing warm up training!!!\n" * 10) # for resnet110 original paper uses lr=0.01 for first 400 minibatches for warm-up # then switch back. In this setup it will correspond for first epoch. # Reference: # Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun # Deep Residual Learning for Image Recognition. arXiv:1512.03385 for param_group in self.optimizer.param_groups: param_group['lr'] = self.args.original_learning_rate ###################################################################################################### ###################################################################################################### valid_loss, valid_acc1, valid_scale, valid_epoch_logits, valid_epoch_metrics = self.validate_epoch() self.scheduler.step() train_intra_logits_mean = statistics.mean(train_epoch_logits["intra"]) train_intra_logits_std = statistics.pstdev(train_epoch_logits["intra"]) train_inter_logits_mean = statistics.mean(train_epoch_logits["inter"]) train_inter_logits_std = statistics.pstdev(train_epoch_logits["inter"]) train_max_probs_mean = statistics.mean(train_epoch_metrics["max_probs"]) train_max_probs_std = statistics.pstdev(train_epoch_metrics["max_probs"]) train_entropies_mean = statistics.mean(train_epoch_metrics["entropies"]) train_entropies_std = statistics.pstdev(train_epoch_metrics["entropies"]) valid_intra_logits_mean = statistics.mean(valid_epoch_logits["intra"]) valid_intra_logits_std = statistics.pstdev(valid_epoch_logits["intra"]) valid_inter_logits_mean = statistics.mean(valid_epoch_logits["inter"]) valid_inter_logits_std = statistics.pstdev(valid_epoch_logits["inter"]) valid_max_probs_mean = statistics.mean(valid_epoch_metrics["max_probs"]) valid_max_probs_std = statistics.pstdev(valid_epoch_metrics["max_probs"]) valid_entropies_mean = statistics.mean(valid_epoch_metrics["entropies"]) valid_entropies_std = statistics.pstdev(valid_epoch_metrics["entropies"]) print("\n####################################################") print("TRAIN MAX PROB MEAN:\t", train_max_probs_mean) print("TRAIN MAX PROB STD:\t", train_max_probs_std) print("VALID MAX PROB MEAN:\t", valid_max_probs_mean) print("VALID MAX PROB STD:\t", valid_max_probs_std) print("####################################################\n") print("\n####################################################") print("TRAIN ENTROPY MEAN:\t", train_entropies_mean) print("TRAIN ENTROPY STD:\t", train_entropies_std) print("VALID ENTROPY MEAN:\t", valid_entropies_mean) print("VALID ENTROPY STD:\t", valid_entropies_std) print("####################################################\n") with open(self.args.executions_raw_results_file_path, "a") as raw_results: raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "LOSS", train_loss)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "ACC1", train_acc1)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "SCALE", train_scale)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "INTRA_LOGITS MEAN", train_intra_logits_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "INTRA_LOGITS STD", train_intra_logits_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "INTER_LOGITS MEAN", train_inter_logits_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "INTER_LOGITS STD", train_inter_logits_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "MAX_PROBS MEAN", train_max_probs_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "MAX_PROBS STD", train_max_probs_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "ENTROPIES MEAN", train_entropies_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "TRAIN", "ENTROPIES STD", train_entropies_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "LOSS", valid_loss)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "ACC1", valid_acc1)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "SCALE", valid_scale)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "INTRA_LOGITS MEAN", valid_intra_logits_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "INTRA_LOGITS STD", valid_intra_logits_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "INTER_LOGITS MEAN", valid_inter_logits_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "INTER_LOGITS STD", valid_inter_logits_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "MAX_PROBS MEAN", valid_max_probs_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "MAX_PROBS STD", valid_max_probs_std)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "ENTROPIES MEAN", valid_entropies_mean)) raw_results.write("{},{},{},{},{},{},{},{}\n".format( self.args.dataset, self.args.model_name, self.args.loss, self.args.execution, self.epoch, "VALID", "ENTROPIES STD", valid_entropies_std)) print() print("TRAIN ==>>\tIALM: {0:.8f}\tIALS: {1:.8f}\tIELM: {2:.8f}\tIELS: {3:.8f}".format( train_intra_logits_mean, train_intra_logits_std, train_inter_logits_mean, train_inter_logits_std)) print("VALID ==>>\tIALM: {0:.8f}\tIALS: {1:.8f}\tIELM: {2:.8f}\tIELS: {3:.8f}".format( valid_intra_logits_mean, valid_intra_logits_std, valid_inter_logits_mean, valid_inter_logits_std)) print() print("\nDATA:", self.args.dataset) print("MODEL:", self.args.model_name) print("LOSS:", self.args.loss, "\n") # if is best if valid_acc1 > best_model_results["VALID ACC1"]: print("!+NEW BEST MODEL VALID ACC1!") best_model_results = { "DATA": self.args.dataset, "MODEL": self.args.model_name, "LOSS": self.args.loss, "EXECUTION": self.args.execution, "EPOCH": self.epoch, "TRAIN LOSS": train_loss, "TRAIN ACC1": train_acc1, "TRAIN SCALE": train_scale, "TRAIN INTRA_LOGITS MEAN": train_intra_logits_mean, "TRAIN INTRA_LOGITS STD": train_intra_logits_std, "TRAIN INTER_LOGITS MEAN": train_inter_logits_mean, "TRAIN INTER_LOGITS STD": train_inter_logits_std, "TRAIN MAX_PROBS MEAN": train_max_probs_mean, "TRAIN MAX_PROBS STD": train_max_probs_std, "TRAIN ENTROPIES MEAN": train_entropies_mean, "TRAIN ENTROPIES STD": train_entropies_std, "VALID LOSS": valid_loss, "VALID ACC1": valid_acc1, "VALID SCALE": valid_scale, "VALID INTRA_LOGITS MEAN": valid_intra_logits_mean, "VALID INTRA_LOGITS STD": valid_intra_logits_std, "VALID INTER_LOGITS MEAN": valid_inter_logits_mean, "VALID INTER_LOGITS STD": valid_inter_logits_std, "VALID MAX_PROBS MEAN": valid_max_probs_mean, "VALID MAX_PROBS STD": valid_max_probs_std, "VALID ENTROPIES MEAN": valid_entropies_mean, "VALID ENTROPIES STD": valid_entropies_std,} print("!+NEW BEST MODEL VALID ACC1:\t\t{0:.4f} IN EPOCH {1}! SAVING {2}\n".format( valid_acc1, self.epoch, self.args.best_model_file_path)) torch.save(self.model.state_dict(), self.args.best_model_file_path) np.save(os.path.join( self.args.experiment_path, "best_model"+str(self.args.execution)+"_train_epoch_logits.npy"), train_epoch_logits) np.save(os.path.join( self.args.experiment_path, "best_model"+str(self.args.execution)+"_train_epoch_metrics.npy"), train_epoch_metrics) np.save(os.path.join( self.args.experiment_path, "best_model"+str(self.args.execution)+"_valid_epoch_logits.npy"), valid_epoch_logits) np.save(os.path.join( self.args.experiment_path, "best_model"+str(self.args.execution)+"_valid_epoch_metrics.npy"), valid_epoch_metrics) print('!$$$$ BEST MODEL TRAIN ACC1:\t\t{0:.4f}'.format(best_model_results["TRAIN ACC1"])) print('!$$$$ BEST MODEL VALID ACC1:\t\t{0:.4f}'.format(best_model_results["VALID ACC1"])) with open(self.args.executions_best_results_file_path, "a") as best_results: best_results.write("{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\n".format( best_model_results["DATA"], best_model_results["MODEL"], best_model_results["LOSS"], best_model_results["EXECUTION"], best_model_results["EPOCH"], best_model_results["TRAIN LOSS"], best_model_results["TRAIN ACC1"], best_model_results["TRAIN SCALE"], best_model_results["TRAIN INTRA_LOGITS MEAN"], best_model_results["TRAIN INTRA_LOGITS STD"], best_model_results["TRAIN INTER_LOGITS MEAN"], best_model_results["TRAIN INTER_LOGITS STD"], best_model_results["TRAIN MAX_PROBS MEAN"], best_model_results["TRAIN MAX_PROBS STD"], best_model_results["TRAIN ENTROPIES MEAN"], best_model_results["TRAIN ENTROPIES STD"], best_model_results["VALID LOSS"], best_model_results["VALID ACC1"], best_model_results["VALID SCALE"], best_model_results["VALID INTRA_LOGITS MEAN"], best_model_results["VALID INTRA_LOGITS STD"], best_model_results["VALID INTER_LOGITS MEAN"], best_model_results["VALID INTER_LOGITS STD"], best_model_results["VALID MAX_PROBS MEAN"], best_model_results["VALID MAX_PROBS STD"], best_model_results["VALID ENTROPIES MEAN"], best_model_results["VALID ENTROPIES STD"],)) print() def train_epoch(self): print() # switch to train mode self.model.train() # Meters loss_meter = utils.MeanMeter() accuracy_meter = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True) epoch_logits = {"intra": [], "inter": []} epoch_metrics = {"max_probs": [], "entropies": [], "max_logits": []} batch_index = 0 for in_data in self.trainset_loader_for_train: batch_index += 1 inputs = in_data[0].cuda() targets = in_data[1].cuda(non_blocking=True) outputs = self.model(inputs) loss, scale, intra_logits, inter_logits = self.criterion(outputs, targets, debug=True) max_logits = outputs.max(dim=1)[0] probabilities = torch.nn.Softmax(dim=1)(outputs) max_probs = probabilities.max(dim=1)[0] entropies = utils.entropies_from_probabilities(probabilities) loss_meter.add(loss.item(), targets.size(0)) accuracy_meter.add(outputs.detach(), targets.detach()) intra_logits = intra_logits.tolist() inter_logits = inter_logits.tolist() if self.args.number_of_model_classes > 100: epoch_logits["intra"] = intra_logits epoch_logits["inter"] = inter_logits else: epoch_logits["intra"] += intra_logits epoch_logits["inter"] += inter_logits epoch_metrics["max_probs"] += max_probs.tolist() epoch_metrics["max_logits"] += max_logits.tolist() epoch_metrics["entropies"] += (entropies/math.log(self.args.number_of_model_classes)).tolist() # normalized entropy!!! # zero grads, compute gradients and do optimizer step self.optimizer.zero_grad() loss.backward() self.optimizer.step() if batch_index % self.args.print_freq == 0: print('Train Epoch: [{0}][{1:3}/{2}]\t' 'Loss {loss:.8f}\t\t' 'Acc1 {acc1_meter:.2f}\t' 'IALM {intra_logits_mean:.4f}\t' 'IALS {intra_logits_std:.8f}\t\t' 'IELM {inter_logits_mean:.4f}\t' 'IELS {inter_logits_std:.8f}' .format(self.epoch, batch_index, len(self.trainset_loader_for_train), loss=loss_meter.avg, acc1_meter=accuracy_meter.value()[0], intra_logits_mean=statistics.mean(intra_logits), intra_logits_std=statistics.stdev(intra_logits), inter_logits_mean=statistics.mean(inter_logits), inter_logits_std=statistics.stdev(inter_logits),)) print('\n#### TRAIN ACC1:\t{0:.4f}\n\n'.format(accuracy_meter.value()[0])) return loss_meter.avg, accuracy_meter.value()[0], scale, epoch_logits, epoch_metrics def validate_epoch(self): print() # switch to evaluate mode self.model.eval() # Meters loss_meter = utils.MeanMeter() accuracy_meter = tnt.meter.ClassErrorMeter(topk=[1], accuracy=True) epoch_logits = {"intra": [], "inter": []} epoch_metrics = {"max_probs": [], "entropies": [], "max_logits": []} with torch.no_grad(): batch_index = 0 for in_data in self.valset_loader: batch_index += 1 inputs = in_data[0].cuda() targets = in_data[1].cuda(non_blocking=True) outputs = self.model(inputs) loss, scale, intra_logits, inter_logits = self.criterion(outputs, targets, debug=True) max_logits = outputs.max(dim=1)[0] probabilities = torch.nn.Softmax(dim=1)(outputs) max_probs = probabilities.max(dim=1)[0] entropies = utils.entropies_from_probabilities(probabilities) loss_meter.add(loss.item(), inputs.size(0)) accuracy_meter.add(outputs.detach(), targets.detach()) intra_logits = intra_logits.tolist() inter_logits = inter_logits.tolist() if self.args.number_of_model_classes > 100: epoch_logits["intra"] = intra_logits epoch_logits["inter"] = inter_logits else: epoch_logits["intra"] += intra_logits epoch_logits["inter"] += inter_logits epoch_metrics["max_probs"] += max_probs.tolist() epoch_metrics["max_logits"] += max_logits.tolist() epoch_metrics["entropies"] += (entropies/math.log(self.args.number_of_model_classes)).tolist() # normalized entropy!!! if batch_index % self.args.print_freq == 0: print('Valid Epoch: [{0}][{1:3}/{2}]\t' 'Loss {loss:.8f}\t\t' 'Acc1 {acc1_meter:.2f}\t' 'IALM {intra_logits_mean:.4f}\t' 'IALS {intra_logits_std:.8f}\t\t' 'IELM {inter_logits_mean:.4f}\t' 'IELS {inter_logits_std:.8f}' .format(self.epoch, batch_index, len(self.valset_loader), loss=loss_meter.avg, acc1_meter=accuracy_meter.value()[0], intra_logits_mean=statistics.mean(intra_logits), intra_logits_std=statistics.stdev(intra_logits), inter_logits_mean=statistics.mean(inter_logits), inter_logits_std=statistics.stdev(inter_logits),)) print('\n#### VALID ACC1:\t{0:.4f}\n\n'.format(accuracy_meter.value()[0])) return loss_meter.avg, accuracy_meter.value()[0], scale, epoch_logits, epoch_metrics

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/utils/procedures.py

import os import pickle import torch.nn.parallel import torch.optim import torch.utils.data import torch import torch.nn.functional as F import csv import numpy as np from sklearn import metrics def compute_weights(iterable): return [sum(iterable) / (iterable[i] * len(iterable)) if iterable[i] != 0 else float("inf") for i in range(len(iterable))] def print_format(iterable): return ["{0:.8f}".format(i) if i is not float("inf") else "{0}".format(i) for i in iterable] def probabilities(outputs): return F.softmax(outputs, dim=1) def max_probabilities(outputs): return F.softmax(outputs, dim=1).max(dim=1)[0] def predictions(outputs): return outputs.argmax(dim=1) def predictions_total(outputs): return outputs.argmax(dim=1).bincount(minlength=outputs.size(1)).tolist() def entropies(outputs): probabilities_log_probabilities = F.softmax(outputs, dim=1) * F.log_softmax(outputs, dim=1) return -1.0 * probabilities_log_probabilities.sum(dim=1) def entropies_grads(outputs): entropy_grads = - (1.0 + F.log_softmax(outputs, dim=1)) return entropy_grads.sum(dim=0).tolist() def cross_entropies(outputs, targets): return - 1.0 * F.log_softmax(outputs, dim=1)[range(outputs.size(0)), targets] def cross_entropies_grads(outputs, targets): cross_entropies_grads = [0 for i in range(outputs.size(1))] for i in range(len(predictions(outputs))): cross_entropies_grads[predictions(outputs)[i]] += - (1.0 - (F.softmax(outputs, dim=1)[i, targets[i]].item())) return cross_entropies_grads def make_equitable(outputs, criterion, weights): weights = torch.Tensor(weights).cuda() weights.requires_grad = False return weights[predictions(outputs)] * criterion[range(outputs.size(0))] def entropies_from_logits(logits): return -(F.softmax(logits, dim=1) * F.log_softmax(logits, dim=1)).sum(dim=1) def entropies_from_probabilities(probabilities): if len(probabilities.size()) == 2: return -(probabilities * torch.log(probabilities)).sum(dim=1) elif len(probabilities.size()) == 3: return -(probabilities * torch.log(probabilities)).sum(dim=2).mean(dim=1) def save_object(object, path, file): with open(os.path.join(path, file + '.pkl'), 'wb') as f: pickle.dump(object, f, pickle.HIGHEST_PROTOCOL) def load_object(path, file): with open(os.path.join(path, file + '.pkl'), 'rb') as f: return pickle.load(f) def save_dict_list_to_csv(dict_list, path, file): with open(os.path.join(path, file + '.csv'), 'w') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=dict_list[0].keys()) writer.writeheader() for dict in dict_list: writer.writerow(dict) def load_dict_list_from_csv(path, file): dict_list = [] with open(os.path.join(path, file + '.csv'), 'r') as csvfile: reader = csv.DictReader(csvfile) for dict in reader: dict_list.append(dict) return dict_list class MeanMeter(object): """computes and stores the current averaged current mean""" def __init__(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def add(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def purity(y_true, y_pred): """compute contingency matrix (also called confusion matrix)""" contingency_matrix = metrics.cluster.contingency_matrix(y_true, y_pred) return np.sum(np.amax(contingency_matrix, axis=0)) / np.sum(contingency_matrix) def asinh(x): return torch.log(x+(x**2+1)**0.5) def acosh(x): return torch.log(x+(x**2-1)**0.5) def atanh(x): return 0.5*torch.log(((1+x)/((1-x)+0.000001))+0.000001) def sinh(x): return (torch.exp(x)-torch.exp(-x))/2 def cosine_similarity(features, prototypes): return F.cosine_similarity(features.unsqueeze(2), prototypes.t().unsqueeze(0), dim=1, eps=1e-6) def mahalanobis_distances(features, prototypes, precisions): diff = features.unsqueeze(2) - prototypes.t().unsqueeze(0) diff2 = features.t().unsqueeze(0) - prototypes.unsqueeze(2) precision_diff = torch.matmul(precisions.unsqueeze(0), diff) extended_product = torch.matmul(diff2.permute(2, 0, 1), precision_diff) mahalanobis_square = torch.diagonal(extended_product, offset=0, dim1=1, dim2=2) mahalanobis = torch.sqrt(mahalanobis_square) return mahalanobis def multiprecisions_mahalanobis_distances(features, prototypes, multiprecisions): mahalanobis_square = torch.Tensor(features.size(0), prototypes.size(0)).cuda() for prototype in range(prototypes.size(0)): diff = features - prototypes[prototype] multiprecisions.unsqueeze(0) diff.unsqueeze(2) precision_diff = torch.matmul(multiprecisions.unsqueeze(0), diff.unsqueeze(2)) product = torch.matmul(diff.unsqueeze(1), precision_diff).squeeze() mahalanobis_square[:, prototype] = product mahalanobis = torch.sqrt(mahalanobis_square) return mahalanobis def rand_bbox(size, lam): W = size[2] H = size[3] #print("calling randbox") cut_rat = np.sqrt(1. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) """ #print("calling randbox") r = 0.5 + np.random.rand(1)/2 s = 0.5/r if np.random.rand(1) < 0.5: r, s = s, r #print(r) #print(s) #print(r * s) cut_w = np.int(W * r) cut_h = np.int(H * s) """ #cx = np.random.randint(W) #cy = np.random.randint(H) cx = np.random.randint(cut_w // 2, high=W - cut_w // 2) cy = np.random.randint(cut_h // 2, high=H - cut_h // 2) bbx1 = np.clip(cx - cut_w // 2, 0, W) bby1 = np.clip(cy - cut_h // 2, 0, H) bbx2 = np.clip(cx + cut_w // 2, 0, W) bby2 = np.clip(cy + cut_h // 2, 0, H) return bbx1, bby1, bbx2, bby2 def print_num_params(model, display_all_modules=False): total_num_params = 0 for n, p in model.named_parameters(): num_params = 1 for s in p.shape: num_params *= s if display_all_modules: print("{}: {}".format(n, num_params)) total_num_params += num_params print("total number of parameters: {:.2e}".format(total_num_params))

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/losses/softmax.py

import torch.nn as nn import torch import math class SoftMaxLossFirstPart(nn.Module): def __init__(self, num_features, num_classes, temperature=1.0): super(SoftMaxLossFirstPart, self).__init__() self.num_features = num_features self.num_classes = num_classes self.temperature = temperature self.weights = nn.Parameter(torch.Tensor(num_classes, num_features)) self.bias = nn.Parameter(torch.Tensor(num_classes)) nn.init.uniform_(self.weights, a=-math.sqrt(1.0/self.num_features), b=math.sqrt(1.0/self.num_features)) nn.init.zeros_(self.bias) def forward(self, features): logits = features.matmul(self.weights.t()) + self.bias # The temperature may be calibrated after training to improve uncertainty estimation. return logits / self.temperature class SoftMaxLossSecondPart(nn.Module): def __init__(self): super(SoftMaxLossSecondPart, self).__init__() self.loss = nn.CrossEntropyLoss() def forward(self, logits, targets, debug=False): loss = self.loss(logits, targets) if not debug: return loss else: targets_one_hot = torch.eye(logits.size(1))[targets].long().cuda() intra_inter_logits = torch.where(targets_one_hot != 0, logits, torch.Tensor([float('Inf')]).cuda()) inter_intra_logits = torch.where(targets_one_hot != 0, torch.Tensor([float('Inf')]).cuda(), logits) intra_logits = intra_inter_logits[intra_inter_logits != float('Inf')] inter_logits = inter_intra_logits[inter_intra_logits != float('Inf')] return loss, 1.0, intra_logits, inter_logits

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/losses/isomaxplus.py

import torch.nn as nn import torch.nn.functional as F import torch class IsoMaxPlusLossFirstPart(nn.Module): """This part replaces the model classifier output layer nn.Linear()""" def __init__(self, num_features, num_classes, temperature=1.0): super(IsoMaxPlusLossFirstPart, self).__init__() self.num_features = num_features self.num_classes = num_classes self.temperature = temperature self.prototypes = nn.Parameter(torch.Tensor(num_classes, num_features)) self.distance_scale = nn.Parameter(torch.Tensor(1)) nn.init.normal_(self.prototypes, mean=0.0, std=1.0) nn.init.constant_(self.distance_scale, 1.0) def forward(self, features): distances = torch.abs(self.distance_scale) * torch.cdist(F.normalize(features), F.normalize(self.prototypes), p=2.0, compute_mode="donot_use_mm_for_euclid_dist") logits = -distances # The temperature may be calibrated after training to improve uncertainty estimation. return logits / self.temperature class IsoMaxPlusLossSecondPart(nn.Module): """This part replaces the nn.CrossEntropyLoss()""" def __init__(self, entropic_scale=10.0): super(IsoMaxPlusLossSecondPart, self).__init__() self.entropic_scale = entropic_scale def forward(self, logits, targets, debug=False): ############################################################################# ############################################################################# """Probabilities and logarithms are calculated separately and sequentially""" """Therefore, nn.CrossEntropyLoss() must not be used to calculate the loss""" ############################################################################# ############################################################################# distances = -logits probabilities_for_training = nn.Softmax(dim=1)(-self.entropic_scale * distances) probabilities_at_targets = probabilities_for_training[range(distances.size(0)), targets] loss = -torch.log(probabilities_at_targets).mean() if not debug: return loss else: targets_one_hot = torch.eye(distances.size(1))[targets].long().cuda() intra_inter_distances = torch.where(targets_one_hot != 0, distances, torch.Tensor([float('Inf')]).cuda()) inter_intra_distances = torch.where(targets_one_hot != 0, torch.Tensor([float('Inf')]).cuda(), distances) intra_distances = intra_inter_distances[intra_inter_distances != float('Inf')] inter_distances = inter_intra_distances[inter_intra_distances != float('Inf')] return loss, 1.0, intra_distances, inter_distances

py

entropic-out-of-distribution-detection

entropic-out-of-distribution-detection-master/losses/isomax.py

import torch.nn as nn import torch.nn.functional as F import torch class IsoMaxLossFirstPart(nn.Module): """This part replaces the model classifier output layer nn.Linear()""" def __init__(self, num_features, num_classes, temperature=1.0): super(IsoMaxLossFirstPart, self).__init__() self.num_features = num_features self.num_classes = num_classes self.temperature = temperature self.prototypes = nn.Parameter(torch.Tensor(num_classes, num_features)) nn.init.constant_(self.prototypes, 0.0) def forward(self, features): distances = torch.cdist(features, self.prototypes, p=2.0, compute_mode="donot_use_mm_for_euclid_dist") logits = -distances # The temperature may be calibrated after training to improve uncertainty estimation. return logits / self.temperature class IsoMaxLossSecondPart(nn.Module): """This part replaces the nn.CrossEntropyLoss()""" def __init__(self, entropic_scale=10.0): super(IsoMaxLossSecondPart, self).__init__() self.entropic_scale = entropic_scale def forward(self, logits, targets, debug=False): ############################################################################# ############################################################################# """Probabilities and logarithms are calculated separately and sequentially""" """Therefore, nn.CrossEntropyLoss() must not be used to calculate the loss""" ############################################################################# ############################################################################# distances = -logits probabilities_for_training = nn.Softmax(dim=1)(-self.entropic_scale * distances) probabilities_at_targets = probabilities_for_training[range(distances.size(0)), targets] loss = -torch.log(probabilities_at_targets).mean() if not debug: return loss else: targets_one_hot = torch.eye(distances.size(1))[targets].long().cuda() intra_inter_distances = torch.where(targets_one_hot != 0, distances, torch.Tensor([float('Inf')]).cuda()) inter_intra_distances = torch.where(targets_one_hot != 0, torch.Tensor([float('Inf')]).cuda(), distances) intra_distances = intra_inter_distances[intra_inter_distances != float('Inf')] inter_distances = inter_intra_distances[inter_intra_distances != float('Inf')] return loss, 1.0, intra_distances, inter_distances

py

stable-continual-learning

stable-continual-learning-master/stable_sgd/main.py

import os import torch import numpy as np import pandas as pd import torch.nn as nn from stable_sgd.models import MLP, ResNet18 from stable_sgd.data_utils import get_permuted_mnist_tasks, get_rotated_mnist_tasks, get_split_cifar100_tasks from stable_sgd.utils import parse_arguments, DEVICE, init_experiment, end_experiment, log_metrics, log_hessian, save_checkpoint def train_single_epoch(net, optimizer, loader, criterion, task_id=None): """ Train the model for a single epoch :param net: :param optimizer: :param loader: :param criterion: :param task_id: :return: """ net = net.to(DEVICE) net.train() for batch_idx, (data, target) in enumerate(loader): data = data.to(DEVICE) target = target.to(DEVICE) optimizer.zero_grad() if task_id: pred = net(data, task_id) else: pred = net(data) loss = criterion(pred, target) loss.backward() optimizer.step() return net def eval_single_epoch(net, loader, criterion, task_id=None): """ Evaluate the model for single epoch :param net: :param loader: :param criterion: :param task_id: :return: """ net = net.to(DEVICE) net.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in loader: data = data.to(DEVICE) target = target.to(DEVICE) # for cifar head if task_id is not None: output = net(data, task_id) else: output = net(data) test_loss += criterion(output, target).item() pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).sum() test_loss /= len(loader.dataset) correct = correct.to('cpu') avg_acc = 100.0 * float(correct.numpy()) / len(loader.dataset) return {'accuracy': avg_acc, 'loss': test_loss} def get_benchmark_data_loader(args): """ Returns the benchmark loader which could be either of these: get_split_cifar100_tasks, get_permuted_mnist_tasks, or get_rotated_mnist_tasks :param args: :return: a function which when called, returns all tasks """ if args.dataset == 'perm-mnist' or args.dataset == 'permuted-mnist': return get_permuted_mnist_tasks elif args.dataset == 'rot-mnist' or args.dataset == 'rotation-mnist': return get_rotated_mnist_tasks elif args.dataset == 'cifar-100' or args.dataset == 'cifar100': return get_split_cifar100_tasks else: raise Exception("Unknown dataset.\n"+ "The code supports 'perm-mnist, rot-mnist, and cifar-100.") def get_benchmark_model(args): """ Return the corresponding PyTorch model for experiment :param args: :return: """ if 'mnist' in args.dataset: if args.tasks == 20 and args.hiddens < 256: print("Warning! the main paper MLP with 256 neurons for experiment with 20 tasks") return MLP(args.hiddens, {'dropout': args.dropout}).to(DEVICE) elif 'cifar' in args.dataset: return ResNet18(config={'dropout': args.dropout}).to(DEVICE) else: raise Exception("Unknown dataset.\n"+ "The code supports 'perm-mnist, rot-mnist, and cifar-100.") def run(args): """ Run a single run of experiment. :param args: please see `utils.py` for arguments and options """ # init experiment acc_db, loss_db, hessian_eig_db = init_experiment(args) # load benchmarks and model print("Loading {} tasks for {}".format(args.tasks, args.dataset)) tasks = get_benchmark_data_loader(args)(args.tasks, args.batch_size) print("loaded all tasks!") model = get_benchmark_model(args) # criterion criterion = nn.CrossEntropyLoss().to(DEVICE) time = 0 for current_task_id in range(1, args.tasks+1): print("================== TASK {} / {} =================".format(current_task_id, args.tasks)) train_loader = tasks[current_task_id]['train'] lr = max(args.lr * args.gamma ** (current_task_id), 0.00005) for epoch in range(1, args.epochs_per_task+1): # 1. train and save optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.8) train_single_epoch(model, optimizer, train_loader, criterion, current_task_id) time += 1 # 2. evaluate on all tasks up to now, including the current task for prev_task_id in range(1, current_task_id+1): # 2.0. only evaluate once a task is finished if epoch == args.epochs_per_task: model = model.to(DEVICE) val_loader = tasks[prev_task_id]['test'] # 2.1. compute accuracy and loss metrics = eval_single_epoch(model, val_loader, criterion, prev_task_id) acc_db, loss_db = log_metrics(metrics, time, prev_task_id, acc_db, loss_db) # 2.2. (optional) compute eigenvalues and eigenvectors of Loss Hessian if prev_task_id == current_task_id and args.compute_eigenspectrum: hessian_eig_db = log_hessian(model, val_loader, time, prev_task_id, hessian_eig_db) # 2.3. save model parameters save_checkpoint(model, time) end_experiment(args, acc_db, loss_db, hessian_eig_db) if __name__ == "__main__": args = parse_arguments() run(args)

py

stable-continual-learning

stable-continual-learning-master/stable_sgd/utils.py

import uuid import torch import argparse import matplotlib import numpy as np import pandas as pd matplotlib.use('Agg') import seaborn as sns from pathlib import Path import matplotlib.pyplot as plt from external_libs.hessian_eigenthings import compute_hessian_eigenthings TRIAL_ID = uuid.uuid4().hex.upper()[0:6] EXPERIMENT_DIRECTORY = './outputs/{}'.format(TRIAL_ID) DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' def parse_arguments(): parser = argparse.ArgumentParser(description='Argument parser') parser.add_argument('--tasks', default=5, type=int, help='total number of tasks') parser.add_argument('--epochs-per-task', default=1, type=int, help='epochs per task') parser.add_argument('--dataset', default='rot-mnist', type=str, help='dataset. options: rot-mnist, perm-mnist, cifar100') parser.add_argument('--batch-size', default=10, type=int, help='batch-size') parser.add_argument('--lr', default=0.1, type=float, help='learning rate') parser.add_argument('--gamma', default=0.4, type=float, help='learning rate decay. Use 1.0 for no decay') parser.add_argument('--dropout', default=0.25, type=float, help='dropout probability. Use 0.0 for no dropout') parser.add_argument('--hiddens', default=256, type=int, help='num of hidden neurons in each layer of a 2-layer MLP') parser.add_argument('--compute-eigenspectrum', default=False, type=bool, help='compute eigenvalues/eigenvectors?') parser.add_argument('--seed', default=1234, type=int, help='random seed') args = parser.parse_args() return args def init_experiment(args): print('------------------- Experiment started -----------------') print(f"Parameters:\n seed={args.seed}\n benchmark={args.dataset}\n num_tasks={args.tasks}\n "+ f"epochs_per_task={args.epochs_per_task}\n batch_size={args.batch_size}\n "+ f"learning_rate={args.lr}\n learning rate decay(gamma)={args.gamma}\n dropout prob={args.dropout}\n") # 1. setup seed for reproducibility torch.manual_seed(args.seed) np.random.seed(args.seed) # 2. create directory to save results Path(EXPERIMENT_DIRECTORY).mkdir(parents=True, exist_ok=True) print("The results will be saved in {}\n".format(EXPERIMENT_DIRECTORY)) # 3. create data structures to store metrics loss_db = {t: [0 for i in range(args.tasks*args.epochs_per_task)] for t in range(1, args.tasks+1)} acc_db = {t: [0 for i in range(args.tasks*args.epochs_per_task)] for t in range(1, args.tasks+1)} hessian_eig_db = {} return acc_db, loss_db, hessian_eig_db def end_experiment(args, acc_db, loss_db, hessian_eig_db): # 1. save all metrics into csv file acc_df = pd.DataFrame(acc_db) acc_df.to_csv(EXPERIMENT_DIRECTORY+'/accs.csv') visualize_result(acc_df, EXPERIMENT_DIRECTORY+'/accs.png') loss_df = pd.DataFrame(loss_db) loss_df.to_csv(EXPERIMENT_DIRECTORY+'/loss.csv') visualize_result(loss_df, EXPERIMENT_DIRECTORY+'/loss.png') hessian_df = pd.DataFrame(hessian_eig_db) hessian_df.to_csv(EXPERIMENT_DIRECTORY+'/hessian_eigs.csv') # 2. calculate average accuracy and forgetting (c.f. ``evaluation`` section in our paper) score = np.mean([acc_db[i][-1] for i in acc_db.keys()]) forget = np.mean([max(acc_db[i])-acc_db[i][-1] for i in range(1, args.tasks)])/100.0 print('average accuracy = {}, forget = {}'.format(score, forget)) print() print('------------------- Experiment ended -----------------') def log_metrics(metrics, time, task_id, acc_db, loss_db): """ Log accuracy and loss at different times of training """ print('epoch {}, task:{}, metrics: {}'.format(time, task_id, metrics)) # log to db acc = metrics['accuracy'] loss = metrics['loss'] loss_db[task_id][time-1] = loss acc_db[task_id][time-1] = acc return acc_db, loss_db def save_eigenvec(filename, arr): """ Save eigenvectors to file """ np.save(filename, arr) def log_hessian(model, loader, time, task_id, hessian_eig_db): """ Compute and log Hessian for a specific task :param model: The PyTorch Model :param loader: Dataloader [to calculate loss and then Hessian] :param time: time is a discrete concept regarding epoch. If we have T tasks each with E epoch, time will be from 0, to (T x E)-1. E.g., if we have 5 tasks with 5 epochs each, then when we finish task 1, time will be 5. :param task_id: Task id (to distiniguish between Hessians of different tasks) :param hessian_eig_db: (The dictionary to store hessians) :return: """ criterion = torch.nn.CrossEntropyLoss().to(DEVICE) use_gpu = True if DEVICE != 'cpu' else False est_eigenvals, est_eigenvecs = compute_hessian_eigenthings( model, loader, criterion, num_eigenthings=3, power_iter_steps=18, power_iter_err_threshold=1e-5, momentum=0, use_gpu=use_gpu, ) key = 'task-{}-epoch-{}'.format(task_id, time-1) hessian_eig_db[key] = est_eigenvals save_eigenvec(EXPERIMENT_DIRECTORY+"/{}-vec.npy".format(key), est_eigenvecs) return hessian_eig_db def save_checkpoint(model, time): """ Save checkpoints of model paramters :param model: pytorch model :param time: int """ filename = '{directory}/model-{trial}-{time}.pth'.format(directory=EXPERIMENT_DIRECTORY, trial=TRIAL_ID, time=time) torch.save(model.cpu().state_dict(), filename) def visualize_result(df, filename): ax = sns.lineplot(data=df, dashes=False) ax.figure.savefig(filename, dpi=250) plt.close()

py

stable-continual-learning

stable-continual-learning-master/stable_sgd/data_utils.py

import numpy as np import torch import torchvision from torch.utils.data import TensorDataset, DataLoader import torchvision.transforms.functional as TorchVisionFunc def get_permuted_mnist(task_id, batch_size): """ Get the dataset loaders (train and test) for a `single` task of permuted MNIST. This function will be called several times for each task. :param task_id: id of the task [starts from 1] :param batch_size: :return: a tuple: (train loader, test loader) """ # convention, the first task will be the original MNIST images, and hence no permutation if task_id == 1: idx_permute = np.array(range(784)) else: idx_permute = torch.from_numpy(np.random.RandomState().permutation(784)) transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor(), torchvision.transforms.Lambda(lambda x: x.view(-1)[idx_permute] ), ]) mnist_train = torchvision.datasets.MNIST('./data/', train=True, download=True, transform=transforms) train_loader = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, num_workers=4, pin_memory=True, shuffle=True) test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./data/', train=False, download=True, transform=transforms), batch_size=256, shuffle=False, num_workers=4, pin_memory=True) return train_loader, test_loader def get_permuted_mnist_tasks(num_tasks, batch_size): """ Returns the datasets for sequential tasks of permuted MNIST :param num_tasks: number of tasks. :param batch_size: batch-size for loaders. :return: a dictionary where each key is a dictionary itself with train, and test loaders. """ datasets = {} for task_id in range(1, num_tasks+1): train_loader, test_loader = get_permuted_mnist(task_id, batch_size) datasets[task_id] = {'train': train_loader, 'test': test_loader} return datasets class RotationTransform: """ Rotation transforms for the images in `Rotation MNIST` dataset. """ def __init__(self, angle): self.angle = angle def __call__(self, x): return TorchVisionFunc.rotate(x, self.angle, fill=(0,)) def get_rotated_mnist(task_id, batch_size): """ Returns the dataset for a single task of Rotation MNIST dataset :param task_id: :param batch_size: :return: """ per_task_rotation = 10 rotation_degree = (task_id - 1)*per_task_rotation rotation_degree -= (np.random.random()*per_task_rotation) transforms = torchvision.transforms.Compose([ RotationTransform(rotation_degree), torchvision.transforms.ToTensor(), ]) train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./data/', train=True, download=True, transform=transforms), batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True) test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('./data/', train=False, download=True, transform=transforms), batch_size=256, shuffle=False, num_workers=4, pin_memory=True) return train_loader, test_loader def get_rotated_mnist_tasks(num_tasks, batch_size): """ Returns data loaders for all tasks of rotation MNIST dataset. :param num_tasks: number of tasks in the benchmark. :param batch_size: :return: """ datasets = {} for task_id in range(1, num_tasks+1): train_loader, test_loader = get_rotated_mnist(task_id, batch_size) datasets[task_id] = {'train': train_loader, 'test': test_loader} return datasets def get_split_cifar100(task_id, batch_size, cifar_train, cifar_test): """ Returns a single task of split CIFAR-100 dataset :param task_id: :param batch_size: :return: """ start_class = (task_id-1)*5 end_class = task_id * 5 targets_train = torch.tensor(cifar_train.targets) target_train_idx = ((targets_train >= start_class) & (targets_train < end_class)) targets_test = torch.tensor(cifar_test.targets) target_test_idx = ((targets_test >= start_class) & (targets_test < end_class)) train_loader = torch.utils.data.DataLoader(torch.utils.data.dataset.Subset(cifar_train, np.where(target_train_idx==1)[0]), batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(torch.utils.data.dataset.Subset(cifar_test, np.where(target_test_idx==1)[0]), batch_size=batch_size) return train_loader, test_loader def get_split_cifar100_tasks(num_tasks, batch_size): """ Returns data loaders for all tasks of split CIFAR-100 :param num_tasks: :param batch_size: :return: """ datasets = {} # convention: tasks starts from 1 not 0 ! # task_id = 1 (i.e., first task) => start_class = 0, end_class = 4 cifar_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),]) cifar_train = torchvision.datasets.CIFAR100('./data/', train=True, download=True, transform=cifar_transforms) cifar_test = torchvision.datasets.CIFAR100('./data/', train=False, download=True, transform=cifar_transforms) for task_id in range(1, num_tasks+1): train_loader, test_loader = get_split_cifar100(task_id, batch_size, cifar_train, cifar_test) datasets[task_id] = {'train': train_loader, 'test': test_loader} return datasets # if __name__ == "__main__": # dataset = get_split_cifar100(1)

py

stable-continual-learning

stable-continual-learning-master/stable_sgd/models.py

import torch import torch.nn as nn from torch.nn.functional import relu, avg_pool2d class MLP(nn.Module): """ Two layer MLP for MNIST benchmarks. """ def __init__(self, hiddens, config): super(MLP, self).__init__() self.W1 = nn.Linear(784, hiddens) self.relu = nn.ReLU(inplace=True) self.dropout_1 = nn.Dropout(p=config['dropout']) self.W2 = nn.Linear(hiddens, hiddens) self.dropout_2 = nn.Dropout(p=config['dropout']) self.W3 = nn.Linear(hiddens, 10) def forward(self, x, task_id=None): x = x.view(-1, 784) out = self.W1(x) out = self.relu(out) out = self.dropout_1(out) out = self.W2(out) out = self.relu(out) out = self.dropout_2(out) out = self.W3(out) return out def conv3x3(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1, config={}): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_planes, planes, stride) self.conv2 = conv3x3(planes, planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), ) self.IC1 = nn.Sequential( nn.BatchNorm2d(planes), nn.Dropout(p=config['dropout']) ) self.IC2 = nn.Sequential( nn.BatchNorm2d(planes), nn.Dropout(p=config['dropout']) ) def forward(self, x): out = self.conv1(x) out = relu(out) out = self.IC1(out) out += self.shortcut(x) out = relu(out) out = self.IC2(out) return out class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes, nf, config={}): super(ResNet, self).__init__() self.in_planes = nf self.conv1 = conv3x3(3, nf * 1) self.bn1 = nn.BatchNorm2d(nf * 1) self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1, config=config) self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2, config=config) self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2, config=config) self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2, config=config) self.linear = nn.Linear(nf * 8 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride, config): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, stride, config=config)) self.in_planes = planes * block.expansion return nn.Sequential(*layers) def forward(self, x, task_id): bsz = x.size(0) out = relu(self.bn1(self.conv1(x.view(bsz, 3, 32, 32)))) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.linear(out) t = task_id offset1 = int((t-1) * 5) offset2 = int(t * 5) if offset1 > 0: out[:, :offset1].data.fill_(-10e10) if offset2 < 100: out[:, offset2:100].data.fill_(-10e10) return out def ResNet18(nclasses=100, nf=20, config={}): net = ResNet(BasicBlock, [2, 2, 2, 2], nclasses, nf, config=config) return net

py

stable-continual-learning

stable-continual-learning-master/external_libs/hessian_eigenthings/lanczos.py

""" Use scipy/ARPACK implicitly restarted lanczos to find top k eigenthings """ import numpy as np import torch from scipy.sparse.linalg import LinearOperator as ScipyLinearOperator from scipy.sparse.linalg import eigsh from warnings import warn def lanczos( operator, num_eigenthings=10, which="LM", max_steps=20, tol=1e-6, num_lanczos_vectors=None, init_vec=None, use_gpu=False, ): """ Use the scipy.sparse.linalg.eigsh hook to the ARPACK lanczos algorithm to find the top k eigenvalues/eigenvectors. Parameters ------------- operator: power_iter.Operator linear operator to solve. num_eigenthings : int number of eigenvalue/eigenvector pairs to compute which : str ['LM', SM', 'LA', SA'] L,S = largest, smallest. M, A = in magnitude, algebriac SM = smallest in magnitude. LA = largest algebraic. max_steps : int maximum number of arnoldi updates tol : float relative accuracy of eigenvalues / stopping criterion num_lanczos_vectors : int number of lanczos vectors to compute. if None, > 2*num_eigenthings init_vec: [torch.Tensor, torch.cuda.Tensor] if None, use random tensor. this is the init vec for arnoldi updates. use_gpu: bool if true, use cuda tensors. Returns ---------------- eigenvalues : np.ndarray array containing `num_eigenthings` eigenvalues of the operator eigenvectors : np.ndarray array containing `num_eigenthings` eigenvectors of the operator """ if isinstance(operator.size, int): size = operator.size else: size = operator.size[0] shape = (size, size) if num_lanczos_vectors is None: num_lanczos_vectors = min(2 * num_eigenthings, size - 1) if num_lanczos_vectors < 2 * num_eigenthings: warn( "[lanczos] number of lanczos vectors should usually be > 2*num_eigenthings" ) def _scipy_apply(x): x = torch.from_numpy(x) if use_gpu: x = x.cuda() return operator.apply(x.float()).cpu().numpy() scipy_op = ScipyLinearOperator(shape, _scipy_apply) if init_vec is None: init_vec = np.random.rand(size) elif isinstance(init_vec, torch.Tensor): init_vec = init_vec.cpu().numpy() eigenvals, eigenvecs = eigsh( A=scipy_op, k=num_eigenthings, which=which, maxiter=max_steps, tol=tol, ncv=num_lanczos_vectors, return_eigenvectors=True, ) return eigenvals, eigenvecs.T

py

stable-continual-learning

stable-continual-learning-master/external_libs/hessian_eigenthings/hvp_operator.py

""" This module defines a linear operator to compute the hessian-vector product for a given pytorch model using subsampled data. """ import torch from .power_iter import Operator, deflated_power_iteration from .lanczos import lanczos class HVPOperator(Operator): """ Use PyTorch autograd for Hessian Vec product calculation model: PyTorch network to compute hessian for dataloader: pytorch dataloader that we get examples from to compute grads loss: Loss function to descend (e.g. F.cross_entropy) use_gpu: use cuda or not max_samples: max number of examples per batch using all GPUs. """ def __init__( self, model, dataloader, criterion, use_gpu=True, full_dataset=True, max_samples=256, ): size = int(sum(p.numel() for p in model.parameters())) super(HVPOperator, self).__init__(size) self.grad_vec = torch.zeros(size) self.model = model if use_gpu: self.model = self.model.cuda() self.dataloader = dataloader # Make a copy since we will go over it a bunch self.dataloader_iter = iter(dataloader) self.criterion = criterion self.use_gpu = use_gpu self.full_dataset = full_dataset self.max_samples = max_samples def apply(self, vec): """ Returns H*vec where H is the hessian of the loss w.r.t. the vectorized model parameters """ if self.full_dataset: return self._apply_full(vec) else: return self._apply_batch(vec) def _apply_batch(self, vec): # compute original gradient, tracking computation graph self.zero_grad() grad_vec = self.prepare_grad() self.zero_grad() # take the second gradient grad_grad = torch.autograd.grad( grad_vec, self.model.parameters(), grad_outputs=vec, only_inputs=True ) # concatenate the results over the different components of the network hessian_vec_prod = torch.cat([g.contiguous().view(-1) for g in grad_grad]) return hessian_vec_prod def _apply_full(self, vec): n = len(self.dataloader) hessian_vec_prod = None for _ in range(n): if hessian_vec_prod is not None: hessian_vec_prod += self._apply_batch(vec) else: hessian_vec_prod = self._apply_batch(vec) hessian_vec_prod = hessian_vec_prod / n return hessian_vec_prod def zero_grad(self): """ Zeros out the gradient info for each parameter in the model """ for p in self.model.parameters(): if p.grad is not None: p.grad.data.zero_() def prepare_grad(self): """ Compute gradient w.r.t loss over all parameters and vectorize """ try: all_inputs, all_targets = next(self.dataloader_iter) except StopIteration: self.dataloader_iter = iter(self.dataloader) all_inputs, all_targets = next(self.dataloader_iter) num_chunks = max(1, len(all_inputs) // self.max_samples) grad_vec = None input_chunks = all_inputs.chunk(num_chunks) target_chunks = all_targets.chunk(num_chunks) for input, target in zip(input_chunks, target_chunks): if self.use_gpu: input = input.cuda() target = target.cuda() output = self.model(input) loss = self.criterion(output, target) grad_dict = torch.autograd.grad( loss, self.model.parameters(), create_graph=True ) if grad_vec is not None: grad_vec += torch.cat([g.contiguous().view(-1) for g in grad_dict]) else: grad_vec = torch.cat([g.contiguous().view(-1) for g in grad_dict]) grad_vec /= num_chunks self.grad_vec = grad_vec return self.grad_vec def compute_hessian_eigenthings( model, dataloader, loss, num_eigenthings=10, full_dataset=True, mode="power_iter", use_gpu=True, max_samples=512, **kwargs ): """ Computes the top `num_eigenthings` eigenvalues and eigenvecs for the hessian of the given model by using subsampled power iteration with deflation and the hessian-vector product Parameters --------------- model : Module pytorch model for this netowrk dataloader : torch.data.DataLoader dataloader with x,y pairs for which we compute the loss. loss : torch.nn.modules.Loss | torch.nn.functional criterion loss function to differentiate through num_eigenthings : int number of eigenvalues/eigenvecs to compute. computed in order of decreasing eigenvalue magnitude. full_dataset : boolean if true, each power iteration call evaluates the gradient over the whole dataset. mode : str ['power_iter', 'lanczos'] which backend to use to compute the top eigenvalues. use_gpu: if true, attempt to use cuda for all lin alg computatoins max_samples: the maximum number of samples that can fit on-memory. used to accumulate gradients for large batches. **kwargs: contains additional parameters passed onto lanczos or power_iter. """ hvp_operator = HVPOperator( model, dataloader, loss, use_gpu=use_gpu, full_dataset=full_dataset, max_samples=max_samples, ) eigenvals, eigenvecs = None, None if mode == "power_iter": eigenvals, eigenvecs = deflated_power_iteration( hvp_operator, num_eigenthings, use_gpu=use_gpu, **kwargs ) elif mode == "lanczos": eigenvals, eigenvecs = lanczos( hvp_operator, num_eigenthings, use_gpu=use_gpu, **kwargs ) else: raise ValueError("Unsupported mode %s (must be power_iter or lanczos)" % mode) return eigenvals, eigenvecs

py

stable-continual-learning

stable-continual-learning-master/external_libs/hessian_eigenthings/power_iter.py

""" This module contains functions to perform power iteration with deflation to compute the top eigenvalues and eigenvectors of a linear operator """ import numpy as np import torch from .utils import log, progress_bar class Operator: """ maps x -> Lx for a linear operator L """ def __init__(self, size): self.size = size def apply(self, vec): """ Function mapping vec -> L vec where L is a linear operator """ raise NotImplementedError class LambdaOperator(Operator): """ Linear operator based on a provided lambda function """ def __init__(self, apply_fn, size): super(LambdaOperator, self).__init__(size) self.apply_fn = apply_fn def apply(self, x): return self.apply_fn(x) def deflated_power_iteration( operator, num_eigenthings=10, power_iter_steps=20, power_iter_err_threshold=1e-4, momentum=0.0, use_gpu=True, to_numpy=True, ): """ Compute top k eigenvalues by repeatedly subtracting out dyads operator: linear operator that gives us access to matrix vector product num_eigenvals number of eigenvalues to compute power_iter_steps: number of steps per run of power iteration power_iter_err_threshold: early stopping threshold for power iteration returns: np.ndarray of top eigenvalues, np.ndarray of top eigenvectors """ eigenvals = [] eigenvecs = [] current_op = operator prev_vec = None def _deflate(x, val, vec): return val * vec.dot(x) * vec log("beginning deflated power iteration") for i in range(num_eigenthings): log("computing eigenvalue/vector %d of %d" % (i + 1, num_eigenthings)) eigenval, eigenvec = power_iteration( current_op, power_iter_steps, power_iter_err_threshold, momentum=momentum, use_gpu=use_gpu, init_vec=prev_vec, ) log("eigenvalue %d: %.4f" % (i + 1, eigenval)) def _new_op_fn(x, op=current_op, val=eigenval, vec=eigenvec): return op.apply(x) - _deflate(x, val, vec) current_op = LambdaOperator(_new_op_fn, operator.size) prev_vec = eigenvec eigenvals.append(eigenval) eigenvec = eigenvec.cpu() if to_numpy: eigenvecs.append(eigenvec.numpy()) else: eigenvecs.append(eigenvec) eigenvals = np.array(eigenvals) eigenvecs = np.array(eigenvecs) # sort them in descending order sorted_inds = np.argsort(eigenvals) eigenvals = eigenvals[sorted_inds][::-1] eigenvecs = eigenvecs[sorted_inds][::-1] return eigenvals, eigenvecs def power_iteration( operator, steps=20, error_threshold=1e-4, momentum=0.0, use_gpu=True, init_vec=None ): """ Compute dominant eigenvalue/eigenvector of a matrix operator: linear Operator giving us matrix-vector product access steps: number of update steps to take returns: (principal eigenvalue, principal eigenvector) pair """ vector_size = operator.size # input dimension of operator if init_vec is None: vec = torch.rand(vector_size) else: vec = init_vec if use_gpu: vec = vec.cuda() prev_lambda = 0.0 prev_vec = torch.randn_like(vec) for i in range(steps): prev_vec = vec / (torch.norm(vec) + 1e-6) new_vec = operator.apply(vec) - momentum * prev_vec # need to handle case where we end up in the nullspace of the operator. # in this case, we are done. if torch.sum(new_vec).item() == 0.0: return 0.0, new_vec lambda_estimate = vec.dot(new_vec).item() diff = lambda_estimate - prev_lambda vec = new_vec.detach() / torch.norm(new_vec) if lambda_estimate == 0.0: # for low-rank error = 1.0 else: error = np.abs(diff / lambda_estimate) progress_bar(i, steps, "power iter error: %.4f" % error) if error < error_threshold: return lambda_estimate, vec prev_lambda = lambda_estimate return lambda_estimate, vec

py

stable-continual-learning

stable-continual-learning-master/external_libs/hessian_eigenthings/spectral_density.py

import numpy as np import torch def _lanczos_step(vec, size, current_draw): pass def lanczos( operator, max_steps=20, tol=1e-6, num_lanczos_vectors=None, init_vec=None, use_gpu=False, ): """ Use the scipy.sparse.linalg.eigsh hook to the ARPACK lanczos algorithm to find the top k eigenvalues/eigenvectors. Parameters ------------- operator: power_iter.Operator linear operator to solve. num_eigenthings : int number of eigenvalue/eigenvector pairs to compute which : str ['LM', SM', 'LA', SA'] L,S = largest, smallest. M, A = in magnitude, algebriac SM = smallest in magnitude. LA = largest algebraic. max_steps : int maximum number of arnoldi updates tol : float relative accuracy of eigenvalues / stopping criterion num_lanczos_vectors : int number of lanczos vectors to compute. if None, > 2*num_eigenthings init_vec: [torch.Tensor, torch.cuda.Tensor] if None, use random tensor. this is the init vec for arnoldi updates. use_gpu: bool if true, use cuda tensors. Returns ---------------- eigenvalues : np.ndarray array containing `num_eigenthings` eigenvalues of the operator eigenvectors : np.ndarray array containing `num_eigenthings` eigenvectors of the operator """ if isinstance(operator.size, int): size = operator.size else: size = operator.size[0] if num_lanczos_vectors is None: num_lanczos_vectors = min(2 * num_eigenthings, size - 1) if num_lanczos_vectors < 2 * num_eigenthings: warn( "[lanczos] number of lanczos vectors should usually be > 2*num_eigenthings" ) return eigenvals

py

snare

snare-master/train.py

import os from pathlib import Path import hydra from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint import numpy as np import random import torch import models from data.dataset import CLIPGraspingDataset from torch.utils.data import DataLoader @hydra.main(config_path="cfgs", config_name="train") def main(cfg): # set random seeds seed = cfg['train']['random_seed'] torch.manual_seed(seed) random.seed(seed) np.random.seed(seed) hydra_dir = Path(os.getcwd()) checkpoint_path = hydra_dir / 'checkpoints' last_checkpoint_path = os.path.join(checkpoint_path, 'last.ckpt') last_checkpoint = last_checkpoint_path \ if os.path.exists(last_checkpoint_path) and cfg['train']['load_from_last_ckpt'] else None checkpoint_callback = ModelCheckpoint( monitor=cfg['wandb']['saver']['monitor'], dirpath=checkpoint_path, filename='{epoch:04d}-{val_acc:.5f}', save_top_k=1, save_last=True, ) trainer = Trainer( gpus=[0], fast_dev_run=cfg['debug'], checkpoint_callback=checkpoint_callback, max_epochs=cfg['train']['max_epochs'], ) # dataset train = CLIPGraspingDataset(cfg, mode='train') valid = CLIPGraspingDataset(cfg, mode='valid') test = CLIPGraspingDataset(cfg, mode='test') # model model = models.names[cfg['train']['model']](cfg, train, valid) # resume epoch and global_steps if last_checkpoint and cfg['train']['load_from_last_ckpt']: print(f"Resuming: {last_checkpoint}") last_ckpt = torch.load(last_checkpoint) trainer.current_epoch = last_ckpt['epoch'] trainer.global_step = last_ckpt['global_step'] del last_ckpt trainer.fit( model, train_dataloader=DataLoader(train, batch_size=cfg['train']['batch_size']), val_dataloaders=DataLoader(valid, batch_size=cfg['train']['batch_size']), ) trainer.test( test_dataloaders=DataLoader(test, batch_size=cfg['train']['batch_size']), ckpt_path='best' ) if __name__ == "__main__": main()

py

snare

snare-master/models/single_cls.py

import numpy as np import json import os from pathlib import Path import torch import torch.nn as nn import torch.nn.functional as F from pytorch_lightning import LightningModule import wandb import models.aggregator as agg class SingleClassifier(LightningModule): def __init__(self, cfg, train_ds, val_ds): self.optimizer = None super().__init__() self.cfg = cfg self.train_ds = train_ds self.val_ds = val_ds self.dropout = self.cfg['train']['dropout'] # input dimensions self.feats_backbone = self.cfg['train']['feats_backbone'] self.img_feat_dim = 512 self.lang_feat_dim = 512 self.num_views = 8 # choose aggregation method agg_cfg = dict(self.cfg['train']['aggregator']) agg_cfg['input_dim'] = self.img_feat_dim self.aggregator_type = self.cfg['train']['aggregator']['type'] self.aggregator = agg.names[self.aggregator_type](agg_cfg) # build network self.build_model() # val progress self.best_val_acc = -1.0 self.best_val_res = None # test progress self.best_test_acc = -1.0 self.best_test_res = None # results save path self.save_path = Path(os.getcwd()) # log with wandb self.log_data = self.cfg['train']['log'] if self.log_data: self.run = wandb.init( project=self._cfg['wandb']['logger']['project'], config=self._cfg['train'], settings=wandb.Settings(show_emoji=False), reinit=True ) wandb.run.name = self._cfg['wandb']['logger']['run_name'] def build_model(self): # image encoder self.img_fc = nn.Sequential( nn.Identity() ) # language encoder self.lang_fc = nn.Sequential( nn.Identity() ) # finetuning layers for classification self.cls_fc = nn.Sequential( nn.Linear(self.img_feat_dim+self.lang_feat_dim, 512), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(512, 256), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(256, 1), ) def configure_optimizers(self): self.optimizer = torch.optim.Adam(self.parameters(), lr=self.cfg['train']['lr']) return self.optimizer def smoothed_cross_entropy(self, pred, target, alpha=0.1): # From ShapeGlot (Achlioptas et. al) # https://github.com/optas/shapeglot/blob/master/shapeglot/models/neural_utils.py n_class = pred.size(1) one_hot = target one_hot = one_hot * ((1.0 - alpha) + alpha / n_class) + (1.0 - one_hot) * alpha / n_class # smoothed log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1) return torch.mean(loss) def _criterion(self, out): probs = out['probs'] labels = out['labels'] loss = self.smoothed_cross_entropy(probs, labels) return { 'loss': loss } def forward(self, batch): (img1_n_feats, img2_n_feats), lang_feats, ans, (key1, key2), annotation, is_visual = batch # to device img1_n_feats = img1_n_feats.to(device=self.device).float() img2_n_feats = img2_n_feats.to(device=self.device).float() lang_feats = lang_feats.to(device=self.device).float() # aggregate img1_feats = self.aggregator(img1_n_feats) img2_feats = self.aggregator(img2_n_feats) # lang encoding lang_enc = self.lang_fc(lang_feats) # normalize if self.cfg['train']['normalize_feats']: img1_feats = img1_feats / img1_feats.norm(dim=-1, keepdim=True) img2_feats = img2_feats / img2_feats.norm(dim=-1, keepdim=True) lang_enc = lang_enc / lang_enc.norm(dim=-1, keepdim=True) # img1 prob img1_enc = self.img_fc(img1_feats) img1_prob = self.cls_fc(torch.cat([img1_enc, lang_enc], dim=-1)) # img2 prob img2_enc = self.img_fc(img2_feats) img2_prob = self.cls_fc(torch.cat([img2_enc, lang_enc], dim=-1)) # cat probs probs = torch.cat([img1_prob, img2_prob], dim=-1) # num steps taken (8 for all views) bs = lang_enc.shape[0] num_steps = torch.ones((bs)).to(dtype=torch.long, device=lang_enc.device) if self.aggregator_type in ['maxpool', 'mean', 'gru']: num_steps = num_steps * 8 elif self.aggregator_type in ['two_random_index']: num_steps = num_steps * 2 test_mode = (ans[0] == -1) if not test_mode: # one-hot labels of answers labels = F.one_hot(ans) return { 'probs': probs, 'labels': labels, 'is_visual': is_visual, 'num_steps': num_steps, } else: return { 'probs': probs, 'num_steps': num_steps, } def training_step(self, batch, batch_idx): out = self.forward(batch) # classifier loss losses = self._criterion(out) if self.log_data: wandb.log({ 'tr/loss': losses['loss'], }) return dict( loss=losses['loss'] ) def check_correct(self, b, labels, probs): right_prob = probs[b][labels[b].argmax()] wrong_prob = probs[b][labels[b].argmin()] correct = right_prob > wrong_prob return correct def validation_step(self, batch, batch_idx): all_view_results = {} for view in range(self.num_views): out = self.forward(batch) losses = self._criterion(out) loss = losses['loss'] probs = out['probs'] labels = out['labels'] visual = out['is_visual'] num_steps = out['num_steps'] probs = F.softmax(probs, dim=-1) metrics = self.compute_metrics(labels, loss, probs, visual, num_steps) all_view_results[view] = metrics mean_val_loss = np.mean([m['val_loss'].detach().cpu().float() for m in all_view_results.values()]) mean_val_acc = np.mean([m['val_acc'] for m in all_view_results.values()]) return dict( val_loss=mean_val_loss, val_acc=mean_val_acc, all_view_results=all_view_results, ) def compute_metrics(self, labels, loss, probs, visual, num_steps): batch_size = probs.shape[0] val_total, val_correct, val_pl_correct = 0, 0, 0. visual_total, visual_correct, pl_visual_correct = 0, 0, 0. nonvis_total, nonvis_correct, pl_nonvis_correct = 0, 0, 0. for b in range(batch_size): correct = self.check_correct(b, labels, probs) if correct: val_correct += 1 val_pl_correct += 1. / num_steps[b] val_total += 1 if bool(visual[b]): if correct: visual_correct += 1 pl_visual_correct += 1. / num_steps[b] visual_total += 1 else: if correct: nonvis_correct += 1 pl_nonvis_correct += 1. / num_steps[b] nonvis_total += 1 val_acc = float(val_correct) / val_total val_pl_acc = float(val_pl_correct) / val_total val_visual_acc = float(visual_correct) / visual_total val_pl_visual_acc = float(pl_visual_correct) / visual_total val_nonvis_acc = float(nonvis_correct) / nonvis_total val_pl_nonvis_acc = float(pl_nonvis_correct) / nonvis_total return dict( val_loss=loss, val_acc=val_acc, val_pl_acc=val_pl_acc, val_correct=val_correct, val_pl_correct=val_pl_correct, val_total=val_total, val_visual_acc=val_visual_acc, val_pl_visual_acc=val_pl_visual_acc, val_visual_correct=visual_correct, val_pl_visual_correct=pl_visual_correct, val_visual_total=visual_total, val_nonvis_acc=val_nonvis_acc, val_pl_nonvis_acc=val_pl_nonvis_acc, val_nonvis_correct=nonvis_correct, val_pl_nonvis_correct=pl_nonvis_correct, val_nonvis_total=nonvis_total, ) def validation_epoch_end(self, all_outputs, mode='vl'): n_view_res = {} sanity_check = True for view in range(self.num_views): view_res = { 'val_loss': 0.0, 'val_correct': 0, 'val_pl_correct': 0, 'val_total': 0, 'val_visual_correct': 0, 'val_pl_visual_correct': 0, 'val_visual_total': 0, 'val_nonvis_correct': 0, 'val_pl_nonvis_correct': 0, 'val_nonvis_total': 0, } for output in all_outputs: metrics = output['all_view_results'][view] view_res['val_loss'] += metrics['val_loss'].item() view_res['val_correct'] += metrics['val_correct'] view_res['val_pl_correct'] += int(metrics['val_pl_correct']) view_res['val_total'] += metrics['val_total'] if view_res['val_total'] > 128: sanity_check = False view_res['val_visual_correct'] += metrics['val_visual_correct'] view_res['val_pl_visual_correct'] += int(metrics['val_pl_visual_correct']) view_res['val_visual_total'] += metrics['val_visual_total'] view_res['val_nonvis_correct'] += metrics['val_nonvis_correct'] view_res['val_pl_nonvis_correct'] += int(metrics['val_pl_nonvis_correct']) view_res['val_nonvis_total'] += metrics['val_nonvis_total'] view_res['val_loss'] = float(view_res['val_loss']) / len(all_outputs) view_res['val_acc'] = float(view_res['val_correct']) / view_res['val_total'] view_res['val_pl_acc'] = float(view_res['val_pl_correct']) / view_res['val_total'] view_res['val_visual_acc'] = float(view_res['val_visual_correct']) / view_res['val_visual_total'] view_res['val_pl_visual_acc'] = float(view_res['val_pl_visual_correct']) / view_res['val_visual_total'] view_res['val_nonvis_acc'] = float(view_res['val_nonvis_correct']) / view_res['val_nonvis_total'] view_res['val_pl_nonvis_acc'] = float(view_res['val_pl_nonvis_correct']) / view_res['val_nonvis_total'] n_view_res[view] = view_res mean_val_loss = np.mean([r['val_loss'] for r in n_view_res.values()]) val_acc = sum([r['val_correct'] for r in n_view_res.values()]) / float(sum([r['val_total'] for r in n_view_res.values()])) val_visual_acc = sum([r['val_visual_correct'] for r in n_view_res.values()]) / float(sum([r['val_visual_total'] for r in n_view_res.values()])) val_nonvis_acc = sum([r['val_nonvis_correct'] for r in n_view_res.values()]) / float(sum([r['val_nonvis_total'] for r in n_view_res.values()])) val_pl_acc = sum([r['val_pl_correct'] for r in n_view_res.values()]) / float(sum([r['val_total'] for r in n_view_res.values()])) val_pl_visual_acc = sum([r['val_pl_visual_correct'] for r in n_view_res.values()]) / float(sum([r['val_visual_total'] for r in n_view_res.values()])) val_pl_nonvis_acc = sum([r['val_pl_nonvis_correct'] for r in n_view_res.values()]) / float(sum([r['val_nonvis_total'] for r in n_view_res.values()])) res = { f'{mode}/loss': mean_val_loss, f'{mode}/acc': val_acc, f'{mode}/acc_visual': val_visual_acc, f'{mode}/acc_nonvis': val_nonvis_acc, f'{mode}/pl_acc': val_pl_acc, f'{mode}/pl_acc_visual': val_pl_visual_acc, f'{mode}/pl_acc_nonvis': val_pl_nonvis_acc, f'{mode}/all_view_res': n_view_res, } if not sanity_check: # only check best conditions and dump data if this isn't a sanity check # test (ran once at the end of training) if mode == 'test': self.best_test_res = dict(res) # val (keep track of best results) else: if val_acc > self.best_val_acc: self.best_val_acc = val_acc self.best_val_res = dict(res) # results to save results_dict = self.best_test_res if mode == 'test' else self.best_val_res best_loss = results_dict[f'{mode}/loss'] best_acc = results_dict[f'{mode}/acc'] best_acc_visual = results_dict[f'{mode}/acc_visual'] best_acc_nonvis = results_dict[f'{mode}/acc_nonvis'] best_pl_acc = results_dict[f'{mode}/pl_acc'] best_pl_acc_visual = results_dict[f'{mode}/pl_acc_visual'] best_pl_acc_nonvis = results_dict[f'{mode}/pl_acc_nonvis'] seed = self.cfg['train']['random_seed'] json_file = os.path.join(self.save_path, f'{mode}-results-{seed}.json') # save results with open(json_file, 'w') as f: json.dump(results_dict, f, sort_keys=True, indent=4) # print best result print("\nBest-----:") print(f'Best {mode} Acc: {best_acc:0.5f} ({best_pl_acc:0.5f}) | Visual {best_acc_visual:0.5f} ({best_pl_acc_visual:0.5f}) | Nonvis: {best_acc_nonvis:0.5f} ({best_pl_acc_nonvis:0.5f}) | Val Loss: {best_loss:0.8f} ') print("------------") if self.log_data: wandb.log(res) return dict( val_loss=mean_val_loss, val_acc=val_acc, val_visual_acc=val_visual_acc, val_nonvis_acc=val_nonvis_acc, val_pl_acc=val_pl_acc, val_pl_visual_acc=val_pl_visual_acc, val_pl_nonvis_acc=val_pl_nonvis_acc, ) def test_step(self, batch, batch_idx): all_view_results = {} for view in range(self.num_views): out = self.forward(batch) probs = out['probs'] num_steps = out['num_steps'] objects = batch[3] annotation = batch[4] probs = F.softmax(probs, dim=-1) pred_ans = probs.argmax(-1) all_view_results[view] = dict( annotation=annotation, objects=objects, pred_ans=pred_ans, num_steps=num_steps, ) return dict( all_view_results=all_view_results, ) def test_epoch_end(self, all_outputs, mode='test'): test_results = {v: list() for v in range(self.num_views)} for out in all_outputs: for view in range(self.num_views): view_res = out['all_view_results'] bs = view_res[view]['pred_ans'].shape[0] for b in range(bs): test_results[view].append({ 'annotation': view_res[view]['annotation'][b], 'objects': ( view_res[view]['objects'][0][b], view_res[view]['objects'][1][b], ), 'pred_ans': int(view_res[view]['pred_ans'][b]), 'num_steps': int(view_res[view]['num_steps'][b]), }) test_pred_save_path = self.save_path if not os.path.exists(test_pred_save_path): os.makedirs(test_pred_save_path) model_type = self.__class__.__name__.lower() json_file = os.path.join(test_pred_save_path, f'{model_type}_test_results.json') with open(json_file, 'w') as f: json.dump(test_results, f, sort_keys=True, indent=4)

py

snare

snare-master/models/zero_shot_cls.py

import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from models.single_cls import SingleClassifier class ZeroShotClassifier(SingleClassifier): def __init__(self, cfg, train_ds, val_ds): super().__init__(cfg, train_ds, val_ds) self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) def build_model(self): pass def configure_optimizers(self): pass def forward(self, batch): (img1_n_feats, img2_n_feats), lang_feats, ans, (key1, key2), annotation, is_visual = batch # to device img1_n_feats = img1_n_feats.to(device=self.device).float() img2_n_feats = img2_n_feats.to(device=self.device).float() lang_feats = lang_feats.to(device=self.device).float() # normalize img1_n_feats = img1_n_feats / img1_n_feats.norm(dim=-1, keepdim=True) img2_n_feats = img2_n_feats / img2_n_feats.norm(dim=-1, keepdim=True) lang_feats = lang_feats / lang_feats.norm(dim=-1, keepdim=True) # aggregate img1_feats = self.aggregator(img1_n_feats) img2_feats = self.aggregator(img2_n_feats) bs = img1_feats.shape[0] probs = [] for b in range(bs): im = torch.stack([img1_feats[b], img2_feats[b]], dim=0) lang = torch.stack([lang_feats[b], lang_feats[b]], dim=0) logit_scale = self.logit_scale.exp() logits_per_image = logit_scale * im @ lang.t() prob = logits_per_image[:,0].softmax(-1) probs.append(prob) # cat probs probs = torch.stack(probs, dim=0) # num steps taken (8 for all views) bs = lang_feats.shape[0] num_steps = torch.ones((bs)).to(dtype=torch.long, device=lang_feats.device) num_steps = num_steps * (self.num_views if self.aggregator_type in ['maxpool', 'mean', 'gru'] else 1) test_mode = (ans[0] == -1) if not test_mode: # one-hot labels of answers labels = F.one_hot(ans) return { 'probs': probs, 'labels': labels, 'is_visual': is_visual, 'num_steps': num_steps, } else: return { 'probs': probs, 'num_steps': num_steps, } def training_step(self, batch, batch_idx): # nothing to train pass def validation_step(self, batch, batch_idx): all_view_results = {} for view in range(8): out = self.forward(batch) losses = self._criterion(out) loss = losses['loss'] probs = out['probs'] labels = out['labels'] visual = out['is_visual'] num_steps = out['num_steps'] metrics = self.compute_metrics(labels, loss, probs, visual, num_steps) all_view_results[view] = metrics mean_val_loss = np.mean([m['val_loss'].detach().cpu().float() for m in all_view_results.values()]) mean_val_acc = np.mean([m['val_acc'] for m in all_view_results.values()]) return dict( val_loss=mean_val_loss, val_acc=mean_val_acc, all_view_results=all_view_results, )

py

snare

snare-master/models/aggregator.py

import torch import torch.nn as nn class MaxPool(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg def forward(self, x): x, _ = x.max(dim=-2) # [B 14 512] -> [B 512] return x class MeanPool(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg def forward(self, x): return x.mean(dim=-2) # [B 14 512] -> [B 512] class RandomIndex(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg def forward(self, x): batch_idxs = torch.randint(x.shape[1], (x.shape[0],)) # [B] return x[torch.arange(0, x.shape[0], dtype=torch.long), batch_idxs] # [B 512] class TwoRandomIndex(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg def forward(self, x): batch_idxs_1 = torch.randint(x.shape[1], (x.shape[0],)) # [B] x1 = x[torch.arange(0, x.shape[0], dtype=torch.long), batch_idxs_1] # [B 512] batch_idxs_2 = torch.randint(x.shape[1], (x.shape[0],)) # [B] x2 = x[torch.arange(0, x.shape[0], dtype=torch.long), batch_idxs_2] # [B 512] x, _ = torch.stack([x1, x2], dim=-1).max(dim=-1) # [B 512] return x names = { 'meanpool': MeanPool, 'maxpool': MaxPool, 'random_index': RandomIndex, 'two_random_index': TwoRandomIndex, }

py

snare

snare-master/models/rotator.py

import numpy as np import collections import json import os import torch import torch.nn as nn import torch.nn.functional as F import wandb from models.single_cls import SingleClassifier class Rotator(SingleClassifier): def __init__(self, cfg, train_ds, val_ds): self.estimate_init_state = False self.estimate_final_state = False self.img_fc = None self.lang_fc = None self.cls_fc = None self.state_fc = None self.action_fc = None super().__init__(cfg, train_ds, val_ds) def build_model(self): # image encoder self.img_fc = nn.Sequential( nn.Identity() ) # language encoder self.lang_fc = nn.Sequential( nn.Identity() ) # finetuning layers for classification self.cls_fc = nn.Sequential( nn.Linear(self.img_feat_dim+self.lang_feat_dim, 512), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(512, 256), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(256, 1), ) # load pre-trained classifier (gets overrided if loading pre-trained rotator) # Note: gets overrided if loading pre-trained rotator model_path = self.cfg['train']['rotator']['pretrained_cls'] checkpoint = torch.load(model_path) self.load_state_dict(checkpoint['state_dict']) print(f"Loaded: {model_path}") self.estimate_init_state = self.cfg['train']['rotator']['estimate_init_state'] self.estimate_final_state = self.cfg['train']['rotator']['estimate_final_state'] # state estimation layers self.state_fc = nn.Sequential( nn.Linear(self.img_feat_dim, 512), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(512, 256), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(256, 128), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(128, 64), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(64, 8) ) # action layers self.action_fc = nn.Sequential( nn.Linear(self.img_feat_dim+self.lang_feat_dim, 512), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(512, 256), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(256, 128), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(128, 64), nn.ReLU(True), nn.Dropout(self.dropout), nn.Linear(64, 8) ) # load pre-trained rotator if self.cfg['train']['pretrained_model']: model_path = self.cfg['train']['pretrained_model'] self.load_state_dict(torch.load(model_path)['state_dict']) print(f"Loaded: {model_path}") def forward(self, batch, teacher_force=True, init_view_force=None): (img1_n_feats, img2_n_feats), lang_feats, ans, (key1, key2), annotation, is_visual = batch # estimate current view init_state_estimation = self.estimate_state(img1_n_feats, img2_n_feats, lang_feats, init_view_force, self.estimate_init_state) # output variables from state estimation bs = img1_n_feats.shape[0] img1_n_feats = init_state_estimation['img1_n_feats'] img2_n_feats = init_state_estimation['img2_n_feats'] lang_feats = init_state_estimation['lang_feats'] init_views1 = init_state_estimation['init_views1'] init_views2 = init_state_estimation['init_views2'] est_init_views1 = init_state_estimation['est_init_views1'] est_init_views2 = init_state_estimation['est_init_views2'] loss = init_state_estimation['loss'] # choose features of ramdomly sampling viewpoints img1_chosen_feats, img2_chosen_feats, rotated_views1, rotated_views2 = self.choose_feats_from_random_views( bs, img1_n_feats, img2_n_feats, init_views1, init_views2) # estimate second view before performing prediction final_state_estimation = self.estimate_state(img1_n_feats, img2_n_feats, lang_feats, [rotated_views1, rotated_views2], self.estimate_final_state) est_final_views1 = final_state_estimation['est_init_views1'] est_final_views2 = final_state_estimation['est_init_views2'] loss += final_state_estimation['loss'] # classifier probablities chosen features img1_chosen_prob = self.cls_fc(torch.cat([img1_chosen_feats, lang_feats], dim=-1)) img2_chosen_prob = self.cls_fc(torch.cat([img2_chosen_feats, lang_feats], dim=-1)) # classifier loss raw_probs = torch.cat([img1_chosen_prob, img2_chosen_prob], dim=-1) probs = F.softmax(raw_probs, dim=-1) bs = lang_feats.shape[0] num_steps = torch.ones((bs)).to(dtype=torch.long, device=lang_feats.device) * 2 test_mode = (ans[0] == -1) if not test_mode: # classifier loss cls_labels = F.one_hot(ans) cls_loss_weight = self.cfg['train']['loss']['cls_weight'] loss += (self.smoothed_cross_entropy(raw_probs, cls_labels)) * cls_loss_weight # put rotated views on device rotated_views1 = rotated_views1.to(device=self.device).int() rotated_views2 = rotated_views2.to(device=self.device).int() # state estimation accuracy est_init_view1_corrects = int(torch.count_nonzero(est_init_views1 == init_views1)) est_init_view2_corrects = int(torch.count_nonzero(est_init_views2 == init_views2)) total_correct_init_view_est = est_init_view1_corrects + est_init_view2_corrects est_final_view1_corrects = int(torch.count_nonzero(est_final_views1 == rotated_views1)) est_final_view2_corrects = int(torch.count_nonzero(est_final_views2 == rotated_views2)) total_correct_final_view_est = est_final_view1_corrects + est_final_view2_corrects # state estimation errors est_err = torch.cat([self.modulo_views(init_views1 - est_init_views1).abs().float(), self.modulo_views(init_views2 - est_init_views2).abs().float()]) est_err += torch.cat([self.modulo_views(rotated_views1 - est_final_views1).abs().float(), self.modulo_views(rotated_views2 - est_final_views2).abs().float()]) est_err = est_err.mean() return { 'probs': probs, 'action_loss': loss, 'labels': cls_labels, 'is_visual': is_visual, 'num_steps': num_steps, 'total_correct_init_view_est': total_correct_init_view_est, 'total_correct_final_view_est': total_correct_final_view_est, 'est_error': est_err, 'est_init_views1': est_init_views1, 'est_init_views2': est_init_views2, 'est_final_views1': est_final_views1, 'est_final_views2': est_final_views2, } else: return { 'probs': probs, 'num_steps': num_steps, } def estimate_state(self, img1_n_feats, img2_n_feats, lang_feats, init_view_force, perform_estimate): # to device img1_n_feats = img1_n_feats.to(device=self.device).float() img2_n_feats = img2_n_feats.to(device=self.device).float() lang_feats = lang_feats.to(device=self.device).float() all_probs = [] bs = img1_n_feats.shape[0] # lang encoding lang_feats = self.lang_fc(lang_feats) # normalize if self.cfg['train']['normalize_feats']: img1_n_feats /= img1_n_feats.norm(dim=-1, keepdim=True) img2_n_feats /= img2_n_feats.norm(dim=-1, keepdim=True) lang_feats /= lang_feats.norm(dim=-1, keepdim=True) # compute single_cls probs for 8 view pairs for v in range(self.num_views): # aggregate img1_feats = img1_n_feats[:, v] img2_feats = img2_n_feats[:, v] # img1 prob img1_feats = self.img_fc(img1_feats) img1_prob = self.cls_fc(torch.cat([img1_feats, lang_feats], dim=-1)) # img2 prob img2_feats = self.img_fc(img2_feats) img2_prob = self.cls_fc(torch.cat([img2_feats, lang_feats], dim=-1)) # cat probs view_probs = torch.cat([img1_prob, img2_prob], dim=-1) all_probs.append(view_probs) all_probs = torch.stack(all_probs, dim=1) all_probs = F.softmax(all_probs, dim=2) # best views with highest classifier probs best_views1 = all_probs[:, :, 0].argmax(-1) best_views2 = all_probs[:, :, 1].argmax(-1) # worst views with lowest classifier probs worst_views1 = all_probs[:, :, 0].argmin(-1) worst_views2 = all_probs[:, :, 0].argmin(-1) # Initialize with worst views if init_view_force == 'adv': init_views1 = worst_views1 init_views2 = worst_views2 else: # initialize with random views if init_view_force is None: init_views1 = torch.randint(self.num_views, (bs,)).cuda() init_views2 = torch.randint(self.num_views, (bs,)).cuda() else: init_views1 = init_view_force[0].to(device=self.device).int() init_views2 = init_view_force[1].to(device=self.device).int() # init features img1_init_feats = torch.stack([img1_n_feats[i, init_views1[i], :] for i in range(bs)]) img2_init_feats = torch.stack([img2_n_feats[i, init_views2[i], :] for i in range(bs)]) gt_init_views1 = F.one_hot(init_views1.to(torch.int64), num_classes=self.num_views) gt_init_views2 = F.one_hot(init_views2.to(torch.int64), num_classes=self.num_views) if perform_estimate: # state estimator est_init_views_logits1 = self.state_fc(img1_init_feats) est_init_views_logits2 = self.state_fc(img2_init_feats) # state estimation loss est_loss_weight = self.cfg['train']['loss']['est_weight'] loss = ((self.smoothed_cross_entropy(est_init_views_logits1, gt_init_views1) + self.smoothed_cross_entropy(est_init_views_logits2, gt_init_views2)) / 2) * est_loss_weight est_init_views1 = F.softmax(est_init_views_logits1, dim=-1).argmax(-1) est_init_views2 = F.softmax(est_init_views_logits2, dim=-1).argmax(-1) else: loss = 0 est_init_views1 = init_views1 est_init_views2 = init_views2 return { 'best_views1': best_views1, 'best_views2': best_views2, 'img1_n_feats': img1_n_feats, 'img2_n_feats': img2_n_feats, 'lang_feats': lang_feats, 'loss': loss, 'init_views1': init_views1, 'init_views2': init_views2, 'est_init_views1': est_init_views1, 'est_init_views2': est_init_views2, } def modulo_views(self, views): bs = views.shape[0] modulo_views = torch.zeros_like(views) for b in range(bs): view = views[b] if view < 4 and view >= -4: modulo_views[b] = view elif view >= 4: modulo_views[b] = -4 + (view % 4) elif view < -4: modulo_views[b] = 4 - (abs(view) % 4) return modulo_views def choose_feats_from_random_views(self, bs, img1_n_feats, img2_n_feats, init_views1, init_views2): rand_next_views = torch.randint(self.num_views, (2, bs)) img1_chosen_feats = torch.stack([img1_n_feats[i, [init_views1[i], rand_next_views[0, i]], :].max(dim=-2)[0] for i in range(bs)]) img2_chosen_feats = torch.stack([img2_n_feats[i, [init_views2[i], rand_next_views[1, i]], :].max(dim=-2)[0] for i in range(bs)]) return img1_chosen_feats, img2_chosen_feats, rand_next_views[0], rand_next_views[1] def compute_metrics(self, labels, loss, probs, visual, num_steps, total_correct_init_view_est, total_correct_final_view_est): batch_size = probs.shape[0] val_total, val_correct, val_pl_correct = 0, 0, 0. visual_total, visual_correct, pl_visual_correct = 0, 0, 0. nonvis_total, nonvis_correct, pl_nonvis_correct = 0, 0, 0. for b in range(batch_size): correct = self.check_correct(b, labels, probs) if correct: val_correct += 1 val_pl_correct += 1. / num_steps[b] val_total += 1 if bool(visual[b]): if correct: visual_correct += 1 pl_visual_correct += 1. / num_steps[b] visual_total += 1 else: if correct: nonvis_correct += 1 pl_nonvis_correct += 1. / num_steps[b] nonvis_total += 1 correct_ests = total_correct_init_view_est + total_correct_final_view_est total_rots = 2 * batch_size val_acc = float(val_correct) / val_total val_pl_acc = float(val_pl_correct) / val_total val_visual_acc = float(visual_correct) / visual_total val_pl_visual_acc = float(pl_visual_correct) / visual_total val_nonvis_acc = float(nonvis_correct) / nonvis_total val_pl_nonvis_acc = float(pl_nonvis_correct) / nonvis_total val_est_init_err = (total_rots - float(total_correct_init_view_est)) / total_rots val_est_final_err = (total_rots - float(total_correct_final_view_est)) / total_rots val_est_err = (2 * total_rots - float(correct_ests)) / (2 * total_rots) return dict( val_loss=loss, val_acc=val_acc, val_pl_acc=val_pl_acc, val_correct=val_correct, val_pl_correct=val_pl_correct, val_total=val_total, val_visual_acc=val_visual_acc, val_pl_visual_acc=val_pl_visual_acc, val_visual_correct=visual_correct, val_pl_visual_correct=pl_visual_correct, val_visual_total=visual_total, val_nonvis_acc=val_nonvis_acc, val_pl_nonvis_acc=val_pl_nonvis_acc, val_nonvis_correct=nonvis_correct, val_pl_nonvis_correct=pl_nonvis_correct, val_nonvis_total=nonvis_total, val_est_init_err=val_est_init_err, val_est_final_err=val_est_final_err, val_est_err=val_est_err ) def training_step(self, batch, batch_idx): out = self.forward(batch, teacher_force=self.cfg['train']['rotator']['teacher_force']) if self.log_data: wandb.log({ 'tr/loss': out['action_loss'], }) return dict( loss=out['action_loss'] ) def validation_step(self, batch, batch_idx): all_view_results = {} views = list(range(self.num_views)) for view in views: # view selection if self.cfg['val']['adversarial_init_view']: out = self.forward(batch, teacher_force=False, init_view_force='adv') else: bs = batch[1].shape[0] # get batch size off lang feats (entry index 1 in batch) init_view_force = [torch.ones((bs,)).int().cuda() * view, torch.ones((bs,)).int().cuda() * view] out = self.forward(batch, teacher_force=False, init_view_force=init_view_force) # losses losses = self._criterion(out) loss = losses['loss'] probs = out['probs'] labels = out['labels'] visual = out['is_visual'] num_steps = out['num_steps'] total_correct_init_view_est = out['total_correct_init_view_est'] total_correct_final_view_est = out['total_correct_final_view_est'] metrics = self.compute_metrics(labels, loss, probs, visual, num_steps, total_correct_init_view_est, total_correct_final_view_est) all_view_results[view] = metrics mean_val_loss = np.mean([m['val_loss'].detach().cpu().float() for m in all_view_results.values()]) mean_val_acc = np.mean([m['val_acc'] for m in all_view_results.values()]) return dict( val_loss=mean_val_loss, val_acc=mean_val_acc, all_view_results=all_view_results, ) def validation_epoch_end(self, all_outputs, mode='vl'): n_view_res = {} views = list(range(self.num_views)) sanity_check = True for view in views: view_res = { 'val_loss': 0.0, 'val_correct': 0, 'val_pl_correct': 0, 'val_total': 0, 'val_visual_correct': 0, 'val_pl_visual_correct': 0, 'val_visual_total': 0, 'val_nonvis_correct': 0, 'val_pl_nonvis_correct': 0, 'val_nonvis_total': 0, 'val_est_init_err': 0.0, 'val_est_final_err': 0.0, 'val_est_err': 0.0, } for output in all_outputs: metrics = output['all_view_results'][view] view_res['val_loss'] += metrics['val_loss'].item() view_res['val_correct'] += metrics['val_correct'] view_res['val_pl_correct'] += int(metrics['val_pl_correct']) view_res['val_total'] += metrics['val_total'] view_res['val_visual_correct'] += metrics['val_visual_correct'] view_res['val_pl_visual_correct'] += int(metrics['val_pl_visual_correct']) view_res['val_visual_total'] += metrics['val_visual_total'] view_res['val_nonvis_correct'] += metrics['val_nonvis_correct'] view_res['val_pl_nonvis_correct'] += int(metrics['val_pl_nonvis_correct']) view_res['val_nonvis_total'] += metrics['val_nonvis_total'] view_res['val_est_init_err'] += metrics['val_est_init_err'] view_res['val_est_final_err'] += metrics['val_est_final_err'] view_res['val_est_err'] += metrics['val_est_err'] view_res['val_loss'] = float(view_res['val_loss']) / len(all_outputs) view_res['val_acc'] = float(view_res['val_correct']) / view_res['val_total'] view_res['val_pl_acc'] = float(view_res['val_pl_correct']) / view_res['val_total'] if view_res['val_total'] > 128: sanity_check = False view_res['val_visual_acc'] = float(view_res['val_visual_correct']) / view_res['val_visual_total'] view_res['val_pl_visual_acc'] = float(view_res['val_pl_visual_correct']) / view_res['val_visual_total'] view_res['val_nonvis_acc'] = float(view_res['val_nonvis_correct']) / view_res['val_nonvis_total'] view_res['val_pl_nonvis_acc'] = float(view_res['val_pl_nonvis_correct']) / view_res['val_nonvis_total'] view_res['val_est_init_err'] = float(view_res['val_est_init_err']) / len(all_outputs) view_res['val_est_final_err'] = float(view_res['val_est_final_err']) / len(all_outputs) view_res['val_est_err'] = float(view_res['val_est_err']) / len(all_outputs) n_view_res[view] = view_res mean_val_loss = np.mean([r['val_loss'] for r in n_view_res.values()]) val_acc = sum([r['val_correct'] for r in n_view_res.values()]) / float(sum([r['val_total'] for r in n_view_res.values()])) val_visual_acc = sum([r['val_visual_correct'] for r in n_view_res.values()]) / float(sum([r['val_visual_total'] for r in n_view_res.values()])) val_nonvis_acc = sum([r['val_nonvis_correct'] for r in n_view_res.values()]) / float(sum([r['val_nonvis_total'] for r in n_view_res.values()])) val_pl_acc = sum([r['val_pl_correct'] for r in n_view_res.values()]) / float(sum([r['val_total'] for r in n_view_res.values()])) val_pl_visual_acc = sum([r['val_pl_visual_correct'] for r in n_view_res.values()]) / float(sum([r['val_visual_total'] for r in n_view_res.values()])) val_pl_nonvis_acc = sum([r['val_pl_nonvis_correct'] for r in n_view_res.values()]) / float(sum([r['val_nonvis_total'] for r in n_view_res.values()])) val_est_err = np.mean([r['val_est_err'] for r in n_view_res.values()]) res = { f'{mode}/loss': mean_val_loss, f'{mode}/acc': val_acc, f'{mode}/acc_visual': val_visual_acc, f'{mode}/acc_nonvis': val_nonvis_acc, f'{mode}/pl_acc': val_pl_acc, f'{mode}/pl_acc_visual': val_pl_visual_acc, f'{mode}/pl_acc_nonvis': val_pl_nonvis_acc, f'{mode}/est_err': val_est_err, f'{mode}/all_view_res': n_view_res, } if not sanity_check: # only check best conditions and dump data if this isn't a sanity check if mode == 'test': self.best_test_res = dict(res) else: if val_acc > self.best_val_acc: self.best_val_acc = val_acc self.best_val_res = dict(res) dump_res = self.best_test_res if mode == 'test' else self.best_val_res # print best result print("\nBest-----:") best_loss = dump_res[f'{mode}/loss'] best_acc = dump_res[f'{mode}/acc'] best_acc_visual = dump_res[f'{mode}/acc_visual'] best_acc_nonvis = dump_res[f'{mode}/acc_nonvis'] best_pl_acc = dump_res[f'{mode}/pl_acc'] best_pl_acc_visual = dump_res[f'{mode}/pl_acc_visual'] best_pl_acc_nonvis = dump_res[f'{mode}/pl_acc_nonvis'] best_est_err = dump_res[f'{mode}/est_err'] seed = self.cfg['train']['random_seed'] json_file = os.path.join(self.save_path, f'{mode}-results-{seed}.json') with open(json_file, 'w') as f: json.dump(dump_res, f, sort_keys=True, indent=4) print(f'Curr Acc: {res[f"{mode}/acc"]:0.5f} ({res[f"{mode}/pl_acc"]:0.5f}) | Visual {res[f"{mode}/acc_visual"]:0.5f} ({res[f"{mode}/pl_acc_visual"]:0.5f}) | Nonvis: {res[f"{mode}/acc_nonvis"]:0.5f} ({res[f"{mode}/pl_acc_nonvis"]:0.5f}) | Avg. Est Err: {res[f"{mode}/est_err"]:0.5f} | Val Loss: {res[f"{mode}/loss"]:0.8f} ') print(f'Best Acc: {best_acc:0.5f} ({best_pl_acc:0.5f}) | Visual {best_acc_visual:0.5f} ({best_pl_acc_visual:0.5f}) | Nonvis: {best_acc_nonvis:0.5f} ({best_pl_acc_nonvis:0.5f}) | Avg. Est Err: {best_est_err:0.5f} | Val Loss: {best_loss:0.8f} ') print("------------") if self.log_data: wandb.log(res) return dict( val_loss=mean_val_loss, val_acc=val_acc, val_visual_acc=val_visual_acc, val_nonvis_acc=val_nonvis_acc, val_pl_acc=val_pl_acc, val_pl_visual_acc=val_pl_visual_acc, val_pl_nonvis_acc=val_pl_nonvis_acc, )

py

snare

snare-master/scripts/extract_clip_features.py

import os import torch from PIL import Image import numpy as np from numpy import asarray import clip import pickle, gzip, json from tqdm import tqdm # Set filepaths shapenet_images_path = './data/shapenet-images/screenshots' ann_files = ["train.json", "val.json", "test.json"] folds = './amt/folds_adversarial' keys = os.listdir(shapenet_images_path) # Load pre-trained CLIP device = "cuda" if torch.cuda.is_available() else "cpu" clip_model, preprocess = clip.load("ViT-B/32", device=device) # Extract CLIP visual features data = {} for key in tqdm(keys): pngs = os.listdir(os.path.join(shapenet_images_path, f"{key}")) pngs = [os.path.join(shapenet_images_path, f"{key}", p) for p in pngs if "png" in p] pngs.sort() for png in pngs: im = Image.open(png) image = preprocess(im).unsqueeze(0).to(device) image_features = clip_model.encode_image(image).squeeze(0).detach().cpu().numpy() image_features = image_features.tolist() name = png.split('/')[-1].replace(".png", "") data[name] = image_features save_path = './data/shapenet-clipViT32-frames.json.gz' json.dump(data, gzip.open(save_path,'wt')) # Extract CLIP language features anns = [] for file in ann_files: fname_rel = os.path.join(folds, file) print(fname_rel) with open(fname_rel, 'r') as f: anns = anns + json.load(f) lang_feat = {} for d in tqdm(anns): ann = d['annotation'] text = clip.tokenize([ann]).to(device) feat = clip_model.encode_text(text) feat = feat.squeeze(0).detach().cpu().numpy() feat = feat.tolist() lang_feat[ann] = feat save_path = './data/langfeat-512-clipViT32.json.gz' json.dump(lang_feat, gzip.open(save_path,'wt'))

py

snare

snare-master/scripts/aggregate_results.py

import argparse import json import os import numpy as np import pandas as pd from scipy.stats import ttest_ind from tqdm import tqdm clip_model_types = ['clip-single_cls-maxpool', 'clip-single_cls-meanpool', 'clip-single_cls-random_index', 'clip-single_cls-two_random_index', 'clip-zero_shot_cls-maxpool', 'clip-zero_shot_cls-meanpool', 'clip-zero_shot_cls-random_index', 'clip-zero_shot_cls-two_random_index'] rotator_model_types = ['clip-rotator-two_random_index'] THRESH = 0.05 welchs_opts = {'equal_var': False, 'alternative': 'two-sided'} def main(args): # Assemble validation and test results. d = [] erroneous_result_files = [] missing_result_files = [] for model_types, prefix_dir, aux in \ [[clip_model_types, "%s_seed_" % args.clip_results_dir_prefix, 'none'], [rotator_model_types, "%s_init_seed_" % args.rotator_results_dir_prefix, 'init'], [rotator_model_types, "%s_final_seed_" % args.rotator_results_dir_prefix, 'final'], [rotator_model_types, "%s_init_final_seed_" % args.rotator_results_dir_prefix, 'both']]: for model in model_types: n_seeds = 1 if 'zero_shot' in model else args.n_seeds # zero-shot models have no inference-time variance for seed in range(n_seeds): # Read in validation results. fn = os.path.join("%s%d" % (prefix_dir, seed), model, 'vl-results-%d.json' % seed) if not os.path.isfile(fn): missing_result_files.append(fn) continue with open(fn, 'r') as f: seed_results = json.load(f) # Check that the result isn't from a pytorch lightning sanity check. if seed_results['vl/all_view_res']['0']['val_total'] < 2000: erroneous_result_files.append(fn) entry = {'model': model, 'aux': aux, 'seed': seed, 'fold': 'val', 'acc': seed_results['vl/acc'], 'acc_nonvis': seed_results['vl/acc_nonvis'], 'acc_visual': seed_results['vl/acc_visual']} d.append(entry) # Compute test results. if args.test_set_answers_fn: fn = os.path.join("%s%d" % (prefix_dir, seed), model, 'test-results-%d.json' % seed) if not os.path.isfile(fn) or args.force_calculate_test_results: # calculate test results model_family = 'zeroshotclassifier' if 'single_cls' in model: model_family = 'singleclassifier' if 'rotator' in model: model_family = 'rotator' results_fn = os.path.join("%s%d" % (prefix_dir, seed), model, '%s_test_results.json' % model_family) if not os.path.isfile(results_fn): missing_result_files.append(results_fn) continue seed_results = compute_test_metrics(args.test_set_answers_fn, results_fn) # Write test results so we don't have to do this again. with open(fn, 'w') as f: json.dump(seed_results, f) else: with open(fn, 'r') as f: seed_results = json.load(f) entry = {'model': model, 'aux': aux, 'seed': seed, 'fold': 'test', 'acc': seed_results['test/acc'], 'acc_nonvis': seed_results['test/acc_nonvis'], 'acc_visual': seed_results['test/acc_visual']} d.append(entry) # Data statistics and tests. df = pd.DataFrame(d) comparisons = [(('clip-single_cls-maxpool', 'none'), ('clip-single_cls-meanpool', 'none')), (('clip-rotator-two_random_index', 'both'), ('clip-single_cls-maxpool', 'none')), (('clip-rotator-two_random_index', 'both'), ('clip-single_cls-two_random_index', 'none')), ] comp_folds = ['val', 'test'] comp_metrics = ['acc'] # , 'acc_nonvis', 'acc_visual'] for fold in comp_folds: print('fold=%s' % fold) for (model_a, aux_a), (model_b, aux_b) in comparisons: print("(%s, %s) compared to (%s, %s)" % (model_a, aux_a, model_b, aux_b)) for metric in comp_metrics: a = df.loc[(df['model'] == model_a) & (df['aux'] == aux_a) & (df['fold'] == fold)][metric] b = df.loc[(df['model'] == model_b) & (df['aux'] == aux_b) & (df['fold'] == fold)][metric] print('\t%s\t\t\t\t\t\tmean\tstd\tN' % metric) print('\t\t%s\t%.3f\t%.3f\t%d' % (model_a, np.mean(a), np.std(a), len(a))) print('\t\t%s\t%.3f\t%.3f\t%d' % (model_b, np.mean(b), np.std(b), len(b))) t, p = ttest_ind(a, b, **welchs_opts) print('\t\t\tp=%f; sig=%d' % (p, 1 if p < THRESH / ((len(comp_folds) * len(comparisons) * len(comp_metrics)) - 1) else 0)) # Subtract one from Bonferroni correction because we don't actually want to run/care about # the maxpool/meanpool comparison on the test fold. # Populate LaTeX table # [model] & [views] & [v viz] & [v nonviz] & [v all] & [t viz] & [t nonviz] & [t all] # CLIP & 360 & 84.5 & 66.1 & 75.3 & 80.0 & 61.4 & 70.9 \\ # \scorer & 360 & \bf 90.6 & \bf 79.3 & \bf 85.0 & \bf 85.9 & 71.3 & \bf 78.7 \\ # \midrule # CLIP & Single & 79.5 & 65.2 & 72.3 & 73.9 & 60.4 & 67.3 \\ # \scorer\ & Single & \bf 89.4 & \bf 75.6 & \bf 82.5 & \bf 84.1 & \bf 69.6 & \bf 77.0 \\ # \midrule # CLIP & Two & 81.7 & 65.5 & 73.6 & 76.2 & 61.0 & 68.8 \\ # \scorer\ & Two & 91.2 & 75.1 & 83.2 & 85.8 & 70.9 & 78.5 \\ # \model\ & Two & \B{91.5} & \B{81.2} & \B{86.3} & \B{86.6} & \B{72.0} & \B{79.4} \\ for comp_set in \ [[['clip-zero_shot_cls-maxpool', 'CLIP', '360-max', 'none'], ['clip-zero_shot_cls-meanpool', 'CLIP', '360-mean', 'none'], ['clip-single_cls-maxpool', '\\scorer', '360-max', 'none'], ['clip-single_cls-meanpool', '\\scorer', '360-mean', 'none']], [['clip-zero_shot_cls-random_index', 'CLIP', 'Single', 'none'], ['clip-single_cls-random_index', '\\scorer', 'Single', 'none']], [['clip-zero_shot_cls-two_random_index', 'CLIP', 'Two', 'none'], ['clip-single_cls-two_random_index', '\\scorer', 'Two', 'none'], ['clip-rotator-two_random_index', '\\model-init', 'Two', 'init'], ['clip-rotator-two_random_index', '\\model-final', 'Two', 'final'], ['clip-rotator-two_random_index', '\\model-both', 'Two', 'both']], ]: for model, model_str, views, aux in comp_set: ss = ['%s & %s' % (model_str, views)] for fold in ['val', 'test']: for metric in ['acc_visual', 'acc_nonvis', 'acc']: a = df.loc[(df['model'] == model) & (df['fold'] == fold) & (df['aux'] == aux)][metric] ss.append('%.1f (%.1f)' % (np.mean(a) * 100., np.std(a) * 100.)) print(' & '.join(ss) + ' \\\\') print('\\midrule') if len(missing_result_files) > 0: print('WARNING: The following results files are expected but were not found; results may shift') print('\n'.join(missing_result_files)) if len(erroneous_result_files) > 0: print('WARNING: The following results files are likely bad perf estimates from PTL sanity checks') print('\n'.join(erroneous_result_files)) # answers_fn - filepath to answers_json # output_fn - filepath to output dump, e.g., zeroshotclassifier_test_results.json def compute_test_metrics(answers_fn, output_fn): # load JSONs with open(answers_fn, 'r') as f: answers = json.load(f) with open(output_fn, 'r') as f: output = json.load(f) num_views = 8 n_view_res = {} mode = 'test' for view in range(num_views): print(f"processing view: {view}") view_res = { 'correct': 0, 'pl_correct': 0, 'total': 0, 'visual_correct': 0, 'pl_visual_correct': 0, 'visual_total': 0, 'nonvis_correct': 0, 'pl_nonvis_correct': 0, 'nonvis_total': 0, } for idx, o in enumerate(tqdm(output[str(view)])): # pdb.set_trace() assert (o['objects'] == answers[idx]['objects']), \ 'Prediction instance does not match answers ' + str(o['objects']) + ' ' + str(answers[idx]['objects']) pred_ans = o['pred_ans'] corr_ans = answers[idx]['ans'] correct = (pred_ans == corr_ans) num_steps = o['num_steps'] is_visual = answers[idx]['visual'] if correct: view_res['correct'] += 1 view_res['pl_correct'] += 1. / num_steps view_res['total'] += 1 if is_visual: if correct: view_res['visual_correct'] += 1 view_res['pl_visual_correct'] += 1. / float(num_steps) view_res['visual_total'] += 1 else: if correct: view_res['nonvis_correct'] += 1 view_res['pl_nonvis_correct'] += 1. / float(num_steps) view_res['nonvis_total'] += 1 view_res['acc'] = float(view_res['correct']) / view_res['total'] view_res['pl_acc'] = float(view_res['pl_correct']) / view_res['total'] view_res['visual_acc'] = float(view_res['visual_correct']) / view_res['visual_total'] view_res['pl_visual_acc'] = float(view_res['pl_visual_correct']) / view_res['visual_total'] view_res['nonvis_acc'] = float(view_res['nonvis_correct']) / view_res['nonvis_total'] view_res['pl_nonvis_acc'] = float(view_res['pl_nonvis_correct']) / view_res['nonvis_total'] n_view_res[view] = view_res acc = sum([r['correct'] for r in n_view_res.values()]) / float(sum([r['total'] for r in n_view_res.values()])) visual_acc = sum([r['visual_correct'] for r in n_view_res.values()]) / float( sum([r['visual_total'] for r in n_view_res.values()])) nonvis_acc = sum([r['nonvis_correct'] for r in n_view_res.values()]) / float( sum([r['nonvis_total'] for r in n_view_res.values()])) pl_acc = sum([r['pl_correct'] for r in n_view_res.values()]) / float(sum([r['total'] for r in n_view_res.values()])) pl_visual_acc = sum([r['pl_visual_correct'] for r in n_view_res.values()]) / float( sum([r['visual_total'] for r in n_view_res.values()])) pl_nonvis_acc = sum([r['pl_nonvis_correct'] for r in n_view_res.values()]) / float( sum([r['nonvis_total'] for r in n_view_res.values()])) res = { f'{mode}/acc': acc, f'{mode}/acc_visual': visual_acc, f'{mode}/acc_nonvis': nonvis_acc, f'{mode}/pl_acc': pl_acc, f'{mode}/pl_acc_visual': pl_visual_acc, f'{mode}/pl_acc_nonvis': pl_nonvis_acc, f'{mode}/all_view_res': n_view_res, } # results to save results_dict = dict(res) best_acc = results_dict[f'{mode}/acc'] best_acc_visual = results_dict[f'{mode}/acc_visual'] best_acc_nonvis = results_dict[f'{mode}/acc_nonvis'] best_pl_acc = results_dict[f'{mode}/pl_acc'] best_pl_acc_visual = results_dict[f'{mode}/pl_acc_visual'] best_pl_acc_nonvis = results_dict[f'{mode}/pl_acc_nonvis'] # print best result print("\nBest-----:") print( f'Best {mode} Acc: {best_acc:0.5f} ({best_pl_acc:0.5f}) | Visual {best_acc_visual:0.5f} ({best_pl_acc_visual:0.5f}) | Nonvis: {best_acc_nonvis:0.5f} ({best_pl_acc_nonvis:0.5f}) ') print("------------") return results_dict if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--clip_results_dir_prefix', type=str, required=True, help='CLIP and MATCH results dir prefix before adding seed') parser.add_argument('--rotator_results_dir_prefix', type=str, required=True, help='Rotator results dir prefix before adding seed and losses') parser.add_argument('--n_seeds', type=int, required=True, help='The number of seeds to index') parser.add_argument('--test_set_answers_fn', type=str, required=False, help='The test set annotations for final test eval; not publicly available') parser.add_argument('--force_calculate_test_results', action='store_true') args = parser.parse_args() main(args)

py

snare

snare-master/data/dataset.py

import os import json import torch import torch.utils.data import numpy as np import gzip import json class CLIPGraspingDataset(torch.utils.data.Dataset): def __init__(self, cfg, mode='train'): self.total_views = 14 self.cfg = cfg self.mode = mode self.folds = os.path.join(self.cfg['data']['amt_data'], self.cfg['data']['folds']) self.feats_backbone = self.cfg['train']['feats_backbone'] self.load_entries() self.load_extracted_features() def load_entries(self): train_train_files = ["train.json"] train_val_files = ["val.json"] test_test_files = ["test.json"] # modes if self.mode == "train": self.files = train_train_files elif self.mode == 'valid': self.files = train_val_files elif self.mode == "test": self.files = test_test_files else: raise RuntimeError('mode not recognized, should be train, valid or test: ' + str(self.mode)) # load amt data self.data = [] for file in self.files: fname_rel = os.path.join(self.folds, file) print(fname_rel) with open(fname_rel, 'r') as f: self.data = self.data + json.load(f) print(f"Loaded Entries. {self.mode}: {len(self.data)} entries") def load_extracted_features(self): if self.feats_backbone == "clip": lang_feats_path = self.cfg['data']['clip_lang_feats'] with gzip.open(lang_feats_path, 'r') as f: self.lang_feats = json.load(f) img_feats_path = self.cfg['data']['clip_img_feats'] with gzip.open(img_feats_path, 'r') as f: self.img_feats = json.load(f) else: raise NotImplementedError() def __len__(self): return len(self.data) def get_img_feats(self, key): feats = [] for i in range(self.total_views): feat = np.array(self.img_feats[f'{key}-{i}']) feats.append(feat) return np.array(feats) def __getitem__(self, idx): entry = self.data[idx] # get keys entry_idx = entry['ans'] if 'ans' in entry else -1 # test set does not contain answers if len(entry['objects']) == 2: key1, key2 = entry['objects'] # fix missing key in pair else: key1 = entry['objects'][entry_idx] while True: key2 = np.random.choice(list(self.img_feats.keys())).split("-")[0] if key2 != key1: break # annotation annotation = entry['annotation'] is_visual = entry['visual'] if 'ans' in entry else -1 # test set does not have labels for visual and non-visual categories # feats start_idx = 6 # discard first 6 views that are top and bottom viewpoints img1_n_feats = torch.from_numpy(self.get_img_feats(key1))[start_idx:] img2_n_feats = torch.from_numpy(self.get_img_feats(key2))[start_idx:] lang_feats = torch.from_numpy(np.array(self.lang_feats[annotation])) # label ans = entry_idx return ( (img1_n_feats, img2_n_feats), lang_feats, ans, (key1, key2), annotation, is_visual, )

py

pySDC

pySDC-master/docs/source/conf.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # pySDC documentation build configuration file, created by # sphinx-quickstart on Tue Oct 11 15:58:40 2016. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. import os import sys sys.path.insert(0, os.path.abspath('../')) sys.path.insert(0, os.path.abspath('../../')) sys.path.insert(0, os.path.abspath('../../../')) sys.path.insert(0, os.path.abspath('../../pySDC')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinxemoji.sphinxemoji', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. # # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'pySDC' copyright = '2023, Robert Speck' author = 'Robert Speck, Thibaut Lunet, Thomas Baumann, Lisa Wimmer' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '5.2' # The full version, including alpha/beta/rc tags. release = '5.2.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. # language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # # today = '' # # Else, today_fmt is used as the format for a strftime call. # # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all # documents. # # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = False toc_object_entries_show_parents = 'hide' # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False suppress_warnings = ['image.nonlocal_uri'] # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' html_theme = 'classic' # Activate the bootstrap theme. # html_theme = 'bootstrap' # html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. # "<project> v<release> documentation" by default. # # html_title = 'pySDC v2' # A shorter title for the navigation bar. Default is the same as html_title. # # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # # html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] html_css_files = [ 'custom.css', ] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # # html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. # # html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. # # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. # # html_additional_pages = {} # If false, no module index is generated. # # html_domain_indices = True # If false, no index is generated. # # html_use_index = True # If true, the index is split into individual pages for each letter. # # html_split_index = False # If true, links to the reST sources are added to the pages. # # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh' # # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. # # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'pySDCdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'pySDC.tex', 'pySDC Documentation', 'Robert Speck', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # # latex_use_parts = False # If true, show page references after internal links. # # latex_show_pagerefs = False # If true, show URL addresses after external links. # # latex_show_urls = False # Documents to append as an appendix to all manuals. # # latex_appendices = [] # It false, will not define \strong, \code, itleref, \crossref ... but only # \sphinxstrong, ..., \sphinxtitleref, ... To help avoid clash with user added # packages. # # latex_keep_old_macro_names = True # If false, no module index is generated. # # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, 'pysdc', 'pySDC Documentation', [author], 1)] # If true, show URL addresses after external links. # # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'pySDC', 'pySDC Documentation', author, 'pySDC', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. # # texinfo_appendices = [] # If false, no module index is generated. # # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # # texinfo_no_detailmenu = False autodoc_mock_imports = ['dolfin', 'mpi4py', 'petsc4py', 'mpi4py_fft', 'cupy']

py

dswgan-paper

dswgan-paper-main/gan_estimation/ldw_gan.py

#wrapper function to save model weights and generate large dataset for #any Lalonde dataset passed import wgan import torch import pandas as pd import numpy as np import ot from hypergrad import AdamHD def wd_distance(real, gen): n = real.shape[0] a = np.ones(n)/n d_gen = ot.emd2(a, a, M=ot.dist(real.to_numpy(), gen.to_numpy(), metric='euclidean'), numItermax=2000000) return d_gen def do_all(df, type, batch_size=128, architecture = [128, 128, 128], lr=1e-4, max_epochs=4000, optimizer=AdamHD, path=""): print(type, "starting training") critic_arch = architecture.copy() critic_arch.reverse() # X | t continuous_vars1 = ["age", "education", "re74", "re75"] continuous_lower_bounds1 = {"re74": 0, "re75": 0} categorical_vars1 = ["black", "hispanic", "married", "nodegree"] context_vars1 = ["t"] # Y | X, t continuous_vars2 = ["re78"] continuous_lower_bounds2 = {"re78": 0} context_vars2 = ["t", "age", "education", "re74", "re75", "black", "hispanic", "married", "nodegree"] df_balanced = df.sample(2*len(df), weights=(1-df.t.mean())*df.t+df.t.mean()*(1-df.t), replace=True, random_state=0) #First X|t data_wrapper1 = wgan.DataWrapper(df_balanced, continuous_vars1, categorical_vars1, context_vars1, continuous_lower_bounds1) x1, context1 = data_wrapper1.preprocess(df_balanced) specifications1 = wgan.Specifications(data_wrapper1, critic_d_hidden=critic_arch, generator_d_hidden=architecture, batch_size=batch_size, optimizer=optimizer, max_epochs=max_epochs, generator_lr=lr, critic_lr=lr, print_every=1e6) generator1 = wgan.Generator(specifications1) critic1 = wgan.Critic(specifications1) #Then Y|X,t data_wrapper2 = wgan.DataWrapper(df_balanced, continuous_vars = continuous_vars2, context_vars= context_vars2, continuous_lower_bounds = continuous_lower_bounds2) x2, context2 = data_wrapper2.preprocess(df_balanced) specifications2 = wgan.Specifications(data_wrapper2, critic_d_hidden=critic_arch, generator_lr=lr, critic_lr=lr, generator_d_hidden=architecture, optimizer=optimizer, batch_size=batch_size, max_epochs=max_epochs,print_every=1e6) generator2 = wgan.Generator(specifications2) critic2 = wgan.Critic(specifications2) df_real = df.copy() G=[generator1,generator2] C=[critic1,critic2] data_wrappers = [data_wrapper1,data_wrapper2] wgan.train(generator1, critic1, x1, context1, specifications1) wgan.train(generator2, critic2, x2, context2, specifications2) df_fake_x = data_wrappers[0].apply_generator(G[0], df.sample(int(1e5), replace=True)) df_fake = data_wrappers[1].apply_generator(G[1], df_fake_x) # Let's also add a counterfactual re78 column to our fake data frame df_fake_x["t"] = 1 - df_fake_x["t"] df_fake["re78_cf"] = data_wrappers[1].apply_generator(G[1], df_fake_x)["re78"] tt = (df_fake.re78 - df_fake.re78_cf).to_numpy()[df_fake.t.to_numpy()==1] print("att =", tt.mean(), "| se =", tt.std()/tt.size**0.5) # Now, we'll compare our fake data to the real data table_groupby = ["t"] scatterplot = dict(x=[], y=[], samples = 400) histogram = dict(variables=['re78', 'black', 'hispanic', 'married', 'nodegree', 're74', 're75', 'education', 'age'], nrow=3, ncol=3) compare_path = path + "compare_"+type wgan.compare_dfs(df_real, df_fake, figsize=5, table_groupby=table_groupby, histogram=histogram, scatterplot=scatterplot,save=True, path=compare_path) df_fake_x = data_wrappers[0].apply_generator(G[0], df.sample(df.shape[0], replace=True)) df_fake = data_wrappers[1].apply_generator(G[1], df_fake_x) print(df_real.columns) df_real = df_real.drop("source",axis=1) wd = wd_distance(df_real, df_fake) print("wd =", wd) for model, name in zip(G + C, ["G_0", "G_1", "C_0", "C_1"]): torch.save(model.state_dict(), path+ name + "_{}.pth".format(type)) n_samples = int(1e6) df_fake_x = data_wrappers[0].apply_generator(G[0], df_balanced.sample(n_samples, replace=True)) df_fake = data_wrappers[1].apply_generator(G[1], df_fake_x) df_fake_x["t"] = 1 - df_fake_x["t"] df_fake["re78_cf"] = data_wrappers[1].apply_generator(G[1], df_fake_x)["re78"] df_fake.to_feather(path+"{}_generated.feather".format(type))

py

dswgan-paper

dswgan-paper-main/monotonicity_penalty/monotonicity.py

import wgan import pandas as pd import torch import numpy as np import torch.nn.functional as F from matplotlib import pyplot as plt ######################################## # setup ######################################## df = pd.read_feather("data/original_data/cps_merged.feather").drop("u75",1).drop("u74",1) df = df.loc[df.t==0,] continuous_vars_0 = ["age", "education", "re74", "re75", "re78"] continuous_lower_bounds_0 = {"re74": 0, "re75": 0, "re78": 0, "age": 0} categorical_vars_0 = ["black", "hispanic", "married", "nodegree"] context_vars_0 = ["t"] dw = wgan.DataWrapper(df, continuous_vars_0, categorical_vars_0, context_vars_0, continuous_lower_bounds_0) x, context = dw.preprocess(df) a = lambda *args, **kwargs: torch.optim.Adam(betas=(0, 0.9), *args, **kwargs) oa = lambda *args, **kwargs: wgan.OAdam(betas=(0, 0.9), *args, **kwargs) spec = wgan.Specifications(dw, batch_size=512, max_epochs=int(3e3), print_every=500, optimizer=a, generator_optimizer=oa, critic_lr=1e-4, generator_lr=1e-4) ######################################## # define penalties ######################################## def monotonicity_penalty_kernreg(factor, h=0.1, idx_out=4, idx_in=0, x_min=None, x_max=None, data_wrapper=None): """ Adds Kernel Regression monotonicity penalty. Incentivizes monotonicity of the mean of cat(x_hat, context)[:, dim_out] conditional on cat(x_hat, context)[:, dim_in]. Parameters ---------- x_hat: torch.tensor generated data context: torch.tensor context data Returns ------- torch.tensor """ if data_wrapper is not None: x_std = torch.cat(data_wrapper.stds, -1).squeeze()[idx_in] x_mean = torch.cat(data_wrapper.means, -1).squeeze()[idx_in] x_min, x_max = ((x-x_mean)/(x_std+1e-3) for x in (x_min, x_max)) if x_min is None: x_min = x.min() if x_max is None: x_max = x.max() def penalty(x_hat, context): y, x = (torch.cat([x_hat, context], -1)[:, idx] for idx in (idx_out, idx_in)) k = lambda x: (1-x.pow(2)).clamp_min(0) x_grid = ((x_max-x_min)*torch.arange(20, device=x.device)/20 + x_min).detach() W = k((x_grid.unsqueeze(-1) - x)/h).detach() W = W/(W.sum(-1, True) + 1e-2) y_mean = (W*y).sum(-1).squeeze() return (factor * (y_mean[:-1]-y_mean[1:])).clamp_min(0).sum() return penalty def monotonicity_penalty_chetverikov(factor, bound=0, idx_out=4, idx_in=0): """ Adds Chetverikov monotonicity test penalty. Incentivizes monotonicity of the mean of cat(x_hat, context)[:, dim_out] conditional on cat(x_hat, context)[:, dim_in]. Parameters ---------- x_hat: torch.tensor generated data context: torch.tensor context data Returns ------- torch.tensor """ def penalty(x_hat, context): y, x = (torch.cat([x_hat, context], -1)[:, idx] for idx in (idx_out, idx_in)) argsort = torch.argsort(x) y, x = y[argsort], x[argsort] sigma = (y[:-1] - y[1:]).pow(2) sigma = torch.cat([sigma, sigma[-1:]]) k = lambda x: 0.75*F.relu(1-x.pow(2)) h_max = torch.tensor((x.max()-x.min()).detach()/2).to(x_hat.device) n = y.size(0) h_min = 0.4*h_max*(np.log(n)/n)**(1/3) l_max = int((h_min/h_max).log()/np.log(0.5)) H = h_max * (torch.tensor([0.5])**torch.arange(l_max)).to(x_hat.device) x_dist = (x.unsqueeze(-1) - x) # i, j Q = k(x_dist.unsqueeze(-1) / H) # i, j, h Q = (Q.unsqueeze(0) * Q.unsqueeze(1)).detach() # i, j, x, h y_dist = (y - y.unsqueeze(-1)) # i, j sgn = torch.sign(x_dist) * (x_dist.abs() > 1e-8) # i, j b = ((y_dist * sgn).unsqueeze(-1).unsqueeze(-1) * Q).sum(0).sum(0) # x, h V = ((sgn.unsqueeze(-1).unsqueeze(-1) * Q).sum(1).pow(2)* sigma.unsqueeze(-1).unsqueeze(-1)).sum(0) # x, h T = b / (V + 1e-2) return T.max().clamp_min(0) * factor return penalty mode = "load" if mode == "train": ######################################## # train and save models ######################################## gennone, critnone = wgan.Generator(spec), wgan.Critic(spec) wgan.train(gennone, critnone, x, context, spec) torch.save(genchet, "monotonicity_penalty/genchet.torch") torch.save(critchet, "monotonicity_penalty/critchet.torch") genkern, critkern = wgan.Generator(spec), wgan.Critic(spec) wgan.train(genkern, critkern, x, context, spec, monotonicity_penalty_kernreg(1, h=1, idx_in=0, idx_out=4, x_min=0, x_max=90, data_wrapper=dw)) torch.save(genkern, "monotonicity_penalty/genkern.torch") torch.save(critkern, "monotonicity_penalty/critkern.torch") genchet, critchet = wgan.Generator(spec), wgan.Critic(spec) wgan.train(genchet, critchet, x, context, spec, monotonicity_penalty_chetverikov(1, idx_in=0, idx_out=4)) torch.save(gennone, "monotonicity_penalty/gennone.torch") torch.save(critnone, "monotonicity_penalty/critnone.torch") elif mode == "load": ######################################## # load models ######################################## genchet = torch.load("monotonicity_penalty/genchet.torch", map_location=torch.device('cpu')) critchet = torch.load("monotonicity_penalty/critchet.torch", map_location=torch.device('cpu')) genkern = torch.load("monotonicity_penalty/genkern.torch", map_location=torch.device('cpu')) critkern = torch.load("monotonicity_penalty/critkern.torch", map_location=torch.device('cpu')) gennone = torch.load("monotonicity_penalty/gennone.torch", map_location=torch.device('cpu')) critnone = torch.load("monotonicity_penalty/critnone.torch", map_location=torch.device('cpu')) ######################################## # produce figures ######################################## # sample data df_none = dw.apply_generator(gennone, df.sample(int(5e5), replace=True)).reset_index(drop=True) df_kern = dw.apply_generator(genkern, df.sample(int(5e5), replace=True)).reset_index(drop=True) df_chet = dw.apply_generator(genchet, df.sample(int(5e5), replace=True)).reset_index(drop=True) # Kernel Smoother for plotting def y_smooth(x, y, h): x, y = torch.tensor(x), torch.tensor(y) k = lambda x: (1-x.pow(2)).clamp_min(0) x_grid = (x.max()-x.min())*torch.arange(20)/20 + x.min() W = k((x_grid.unsqueeze(-1) - x)/h) W = W/W.sum(-1, True) return x_grid, (W*y).sum(-1) # Compare conditional means plt.figure(figsize=(10, 6)) for df_, lab in zip((df, df_none, df_kern, df_chet), ("Original Data", "Unpenalized WGAN", "Kernel Regression Penalty", "Chetverikov Penalty")): x_, y = df_.age.to_numpy(), df_.re78.to_numpy() x_grid, y_hat = y_smooth(x_, y, 1) plt.plot(x_grid, y_hat, label=lab) plt.ylabel("Earnings 1978") plt.xlabel("Age") plt.legend() plt.savefig("figures/monotonicity.pdf", format="pdf") # Compare overall fits f, a = plt.subplots(4, 6, figsize=(15, 10), sharex="col", sharey="col") for i, (ax, df_, n) in enumerate(zip(a, [df, df_none, df_kern, df_chet], ["Original", "Unpenalized WGAN", "Kernel Regression Penalty", "Chetverikov Penalty"])): ax[0].set_ylabel(n) ax[0].matshow(df_.drop(["t"], 1).corr()) ax[1].hist(df_.re78, density=True) ax[2].hist(df_.age, density=True) ax[3].hist(df_.re74, density=True) ax[4].hist(df_.education, density=True) ax[5].hist(df_.married, density=True) for _ in range(1,6): ax[_].set_yticklabels([]) for i, n in enumerate(["Correlation", "Earnings 1978", "Age", "Earnings 1974", "Education", "Married"]): a[0, i].set_title(n) plt.savefig("figures/monotonicity_fit.pdf", format="pdf")

py

HC-MGAN

HC-MGAN-main/fmnist.py

import argparse import os import sys from utils.data import create_dataloader, merge_dataloaders from tree.tree import Node, grow_tree_from_root import torch parser = argparse.ArgumentParser() #main config parser.add_argument('--dataset_path', type=str, default='data', metavar='', help='Path for folder containing the dataset root folder') parser.add_argument('--logs_path', type=str, default='experiment_logs_fmnist', metavar='', help='Folder for saving all logs (replaces previous logs in the folder if any)') parser.add_argument('--root_node_name', type=str, default='Z', metavar='', help='Name for the root node of the tree') parser.add_argument('--device', type=int, default=0, metavar='', help='GPU device to be used') parser.add_argument('--amp_enable', action='store_true', help='Enables automatic mixed precision if available (executes faster on modern GPUs') parser.set_defaults(amp_enable=False) #architecture/model parameters parser.add_argument('--nf_g', type=int, default=128, metavar='', help='Number of feature maps for generator.') parser.add_argument('--nf_d', type=int, default=128, metavar='', help='Number of feature maps for discriminator/classifier.') parser.add_argument('--kernel_size_g', type=int, default=4, metavar='', help='Size of kernel for generators') parser.add_argument('--kernel_size_d', type=int, default=5, metavar='', help='Size of kernel for discriminator/classifier') parser.add_argument('--normalization_d', type=str, default='layer_norm', metavar='', help='Type of normalization layer used for discriminator/classifier') parser.add_argument('--normalization_g', type=str, default='no_norm', metavar='', help='Type of normalization layer used for generator') parser.add_argument('--architecture_d', type=str, default='cnn', metavar='', help='Specific architecture choice for for discriminator/classifier') parser.add_argument('--architecture_g', type=str, default='cnn', metavar='', help='Specific architecture choice for for generator') parser.add_argument('--img_channels', type=int, default=1, metavar='', help='Number of channels used for intended types of images') parser.add_argument('--latent_dim', type=int, default=100, metavar='', help="Dimension of generator's latent space") parser.add_argument('--batch_size_real', type=int, default=100, metavar='', help="Minibatch size for real images") parser.add_argument('--batch_size_gen', type=int, default=100, metavar='', help="Minibatch size for generated images ") parser.add_argument('--img_dim', type=int, default=28, metavar='', help="Image dimensions") parser.add_argument('--shared_features_across_ref', action='store_true', help='Shares encoder features among parallel refinement groups (inactivated by default)') parser.set_defaults(shared_features_across_ref=False) #training parameters parser.add_argument('--lr_d', type=float, default=0.0001, metavar='', help='Learning rate for discriminator') parser.add_argument('--lr_c', type=float, default=0.00002, metavar='', help='Learning rate for classifier') parser.add_argument('--lr_g', type=float, default=0.0002, metavar='', help='Learning rate for generator') parser.add_argument('--b1', type=float, default=0.5, metavar='', help='Adam optimizer beta 1 parameter') parser.add_argument('--b2', type=float, default=0.999, metavar='', help='Adam optimizer beta 2 parameter') parser.add_argument('--noise_start', type=float, default=1.5, metavar='', help='Start image noise intensity linearly decaying throughout each GAN/MGAN training') parser.add_argument('--epochs_raw_split', type=int, default=150, metavar='', help='Number of epochs for raw split training') parser.add_argument('--epochs_refinement', type=int, default=150, metavar='', help='Number of epochs for refinement training') parser.add_argument('--diversity_parameter_g', type=float, default=1.0, metavar='', help="Hyperparameter for weighting generators' classification loss component") parser.add_argument('--no_refinements', type=int, default=8, metavar='', help='Number of refinements in each split') parser.add_argument('--no_splits', type=int, default=9, metavar='', help='Number of splits during tree growth') parser.add_argument('--collapse_check_epoch', type=float, default=40, metavar='', help='Epoch after which to check for generation collapse') parser.add_argument('--sample_interval', type=int, default=10, metavar='', help='No. of epochs between printring/saving training logs') parser.add_argument('--min_prob_mass_variation', type=float, default=150, metavar='', help='If the total prob mass variation between two consecutive refinements is less than this number, to save up time, the next refinements are skipped for that node') args = parser.parse_args() torch.cuda.set_device(args.device) dataloader_train = create_dataloader(dataset='fmnist', test=False, batch_size=args.batch_size_real, path=args.dataset_path) dataloader_test = create_dataloader(dataset='fmnist', test=True, batch_size=args.batch_size_real, path=args.dataset_path) dataloader_train = merge_dataloaders(dataloader_train, dataloader_test) root_node = Node(args.root_node_name, dataloader_train.sampler.weights, args.logs_path) grow_tree_from_root(root_node, dataloader_train, args)

py

HC-MGAN

HC-MGAN-main/sop.py

import argparse import os import sys from utils.data import create_dataloader, merge_dataloaders from tree.tree import Node, grow_tree_from_root import torch parser = argparse.ArgumentParser() #main config parser.add_argument('--dataset_path', type=str, default='data', metavar='', help='Path for folder containing the dataset root folder') parser.add_argument('--logs_path', type=str, default='experiment_logs_sop', metavar='', help='Folder for saving all logs (replaces previous logs in the folder if any)') parser.add_argument('--root_node_name', type=str, default='Z', metavar='', help='Name for the root node of the tree') parser.add_argument('--device', type=int, default=0, metavar='', help='GPU device to be used') parser.add_argument('--amp_enable', action='store_true', help='Enables automatic mixed precision if available (executes faster on modern GPUs') parser.set_defaults(amp_enable=False) #architecture/model parameters parser.add_argument('--nf_g', type=int, default=128, metavar='', help='Number of feature maps for generator.') parser.add_argument('--nf_d', type=int, default=128, metavar='', help='Number of feature maps for discriminator/classifier.') parser.add_argument('--kernel_size_g', type=int, default=4, metavar='', help='Size of kernel for generators') parser.add_argument('--kernel_size_d', type=int, default=5, metavar='', help='Size of kernel for discriminator/classifier') parser.add_argument('--normalization_d', type=str, default='layer_norm', metavar='', help='Type of normalization layer used for discriminator/classifier') parser.add_argument('--normalization_g', type=str, default='no_norm', metavar='', help='Type of normalization layer used for generator') parser.add_argument('--architecture_d', type=str, default='cnn', metavar='', help='Specific architecture choice for for discriminator/classifier') parser.add_argument('--architecture_g', type=str, default='cnn', metavar='', help='Specific architecture choice for for generator') parser.add_argument('--img_channels', type=int, default=1, metavar='', help='Number of channels used for intended types of images') parser.add_argument('--latent_dim', type=int, default=100, metavar='', help="Dimension of generator's latent space") parser.add_argument('--batch_size_real', type=int, default=100, metavar='', help="Minibatch size for real images") parser.add_argument('--batch_size_gen', type=int, default=100, metavar='', help="Minibatch size for generated images ") parser.add_argument('--img_dim', type=int, default=32, metavar='', help="Image dimensions") parser.add_argument('--shared_features_across_ref', action='store_true', help='Shares encoder features among parallel refinement groups (activated by default)') parser.add_argument('--no-shared_features_across_ref', dest='shared_features_across_ref', action='store_false', help='Does not share encoder features among parallel refinement groups') parser.set_defaults(shared_features_across_ref=True) #training parameters parser.add_argument('--lr_d', type=float, default=0.0002, metavar='', help='Learning rate for discriminator') parser.add_argument('--lr_c', type=float, default=0.00002, metavar='', help='Learning rate for classifier') parser.add_argument('--lr_g', type=float, default=0.0002, metavar='', help='Learning rate for generator') parser.add_argument('--b1', type=float, default=0.5, metavar='', help='Adam optimizer beta 1 parameter') parser.add_argument('--b2', type=float, default=0.999, metavar='', help='Adam optimizer beta 2 parameter') parser.add_argument('--noise_start', type=float, default=1.0, metavar='', help='Start image noise intensity linearly decaying throughout each GAN/MGAN training') parser.add_argument('--epochs_raw_split', type=int, default=100, metavar='', help='Number of epochs for raw split training') parser.add_argument('--epochs_refinement', type=int, default=100, metavar='', help='Number of epochs for refinement training') parser.add_argument('--diversity_parameter_g', type=float, default=0.1, metavar='', help="Hyperparameter for weighting generators' classification loss component") parser.add_argument('--no_refinements', type=int, default=4, metavar='', help='Number of refinements in each split') parser.add_argument('--no_splits', type=int, default=9, metavar='', help='Number of splits during tree growth') parser.add_argument('--collapse_check_epoch', type=float, default=40, metavar='', help='Epoch after which to check for generation collapse') parser.add_argument('--sample_interval', type=int, default=10, metavar='', help='No. of epochs between printring/saving training logs') parser.add_argument('--min_prob_mass_variation', type=float, default=150, metavar='', help='If the total prob mass variation between two consecutive refinements is less than this number, to save up time, the next refinements are skipped for that node') args = parser.parse_args() torch.cuda.set_device(args.device) dataloader_train = create_dataloader(dataset='sop', test=False, batch_size=args.batch_size_real, path=args.dataset_path) root_node = Node(args.root_node_name, dataloader_train.sampler.weights, args.logs_path) grow_tree_from_root(root_node, dataloader_train, args)

py

HC-MGAN

HC-MGAN-main/mnist.py

import argparse import os import sys from utils.data import create_dataloader, merge_dataloaders from tree.tree import Node, grow_tree_from_root import torch parser = argparse.ArgumentParser() #omain config parser.add_argument('--dataset_path', type=str, default='data', metavar='', help='Path for folder containing the dataset root folder') parser.add_argument('--logs_path', type=str, default='experiment_logs_mnist', metavar='', help='Folder for saving all logs (replaces previous logs in the folder if any)') parser.add_argument('--root_node_name', type=str, default='Z', metavar='', help='Name for the root node of the tree') parser.add_argument('--device', type=int, default=0, metavar='', help='GPU device to be used') parser.add_argument('--amp_enable', action='store_true', help='Enables automatic mixed precision if available (executes faster on modern GPUs') parser.set_defaults(amp_enable=False) #architecture/model parameters parser.add_argument('--nf_g', type=int, default=128, metavar='', help='Number of feature maps for generator.') parser.add_argument('--nf_d', type=int, default=128, metavar='', help='Number of feature maps for discriminator/classifier.') parser.add_argument('--kernel_size_g', type=int, default=4, metavar='', help='Size of kernel for generators') parser.add_argument('--kernel_size_d', type=int, default=5, metavar='', help='Size of kernel for discriminator/classifier') parser.add_argument('--normalization_d', type=str, default='layer_norm', metavar='', help='Type of normalization layer used for discriminator/classifier') parser.add_argument('--normalization_g', type=str, default='no_norm', metavar='', help='Type of normalization layer used for generator') parser.add_argument('--architecture_d', type=str, default='cnn', metavar='', help='Specific architecture choice for for discriminator/classifier') parser.add_argument('--architecture_g', type=str, default='cnn', metavar='', help='Specific architecture choice for for generator') parser.add_argument('--img_channels', type=int, default=1, metavar='', help='Number of channels used for intended types of images') parser.add_argument('--latent_dim', type=int, default=100, metavar='', help="Dimension of generator's latent space") parser.add_argument('--batch_size_real', type=int, default=100, metavar='', help="Minibatch size for real images") parser.add_argument('--batch_size_gen', type=int, default=100, metavar='', help="Minibatch size for generated images ") parser.add_argument('--img_dim', type=int, default=28, metavar='', help="Image dimensions") parser.add_argument('--shared_features_across_ref', action='store_true', help='Shares encoder features among parallel refinement groups (inactivated by default)') parser.set_defaults(shared_features_across_ref=False) #training parameters parser.add_argument('--lr_d', type=float, default=0.0001, metavar='', help='Learning rate for discriminator') parser.add_argument('--lr_c', type=float, default=0.00002, metavar='', help='Learning rate for classifier') parser.add_argument('--lr_g', type=float, default=0.0002, metavar='', help='Learning rate for generator') parser.add_argument('--b1', type=float, default=0.5, metavar='', help='Adam optimizer beta 1 parameter') parser.add_argument('--b2', type=float, default=0.999, metavar='', help='Adam optimizer beta 2 parameter') parser.add_argument('--noise_start', type=float, default=1.0, metavar='', help='Start image noise intensity linearly decaying throughout each GAN/MGAN training') parser.add_argument('--epochs_raw_split', type=int, default=100, metavar='', help='Number of epochs for raw split training') parser.add_argument('--epochs_refinement', type=int, default=100, metavar='', help='Number of epochs for refinement training') parser.add_argument('--diversity_parameter_g', type=float, default=1.0, metavar='', help="Hyperparameter for weighting generators' classification loss component") parser.add_argument('--no_refinements', type=int, default=6, metavar='', help='Number of refinements in each split') parser.add_argument('--no_splits', type=int, default=9, metavar='', help='Number of splits during tree growth') parser.add_argument('--collapse_check_epoch', type=float, default=40, metavar='', help='Epoch after which to check for generation collapse') parser.add_argument('--sample_interval', type=int, default=10, metavar='', help='No. of epochs between printring/saving training logs') parser.add_argument('--min_prob_mass_variation', type=float, default=150, metavar='', help='If the total prob mass variation between two consecutive refinements is less than this number, to save up time, the next refinements are skipped for that node') args = parser.parse_args() torch.cuda.set_device(args.device) dataloader_train = create_dataloader(dataset='mnist', test=False, batch_size=args.batch_size_real, path=args.dataset_path) dataloader_test = create_dataloader(dataset='mnist', test=True, batch_size=args.batch_size_real, path=args.dataset_path) dataloader_train = merge_dataloaders(dataloader_train, dataloader_test) root_node = Node(args.root_node_name, dataloader_train.sampler.weights, args.logs_path) grow_tree_from_root(root_node, dataloader_train, args)

py

HC-MGAN

HC-MGAN-main/models/models_32x32.py

import argparse import os from torch.autograd import Variable import torch.nn as nn import torch from models.utils import verify_string_args, linear_block, Reshape, convT_block, conv_block class Generator(nn.Module): def __init__(self, architecture = 'cnn', nf=128, kernel_size=4, latent_dim = 100, nc = 3, print_shapes=False, norm = 'no_norm' ): super(Generator, self).__init__() print_shapes = False architecture_list = ['cnn', 'cnn_short', 'cnn_long'] normalization_list = ['no_norm'] verify_string_args(architecture, architecture_list) verify_string_args(norm, normalization_list) self.img_size = 32 self.architecture = architecture self.nf = nf self.kernel_size = kernel_size self.latent_dim = latent_dim self.nc = nc self.norm = norm #print('Generator normalization is ', self.norm) gen_layers = [] if architecture == 'cnn' or architecture == 'cnn_short': first_map_shape = 8 gen_layers += linear_block(self.latent_dim, nf*2*first_map_shape*first_map_shape, norm='no_norm', act=nn.ReLU(True)) gen_layers += Reshape(-1, nf*2, first_map_shape, first_map_shape), gen_layers += convT_block(nf*2, nf, stride=2, padding=1, norm=self.norm, act=nn.ReLU(True)) gen_layers += convT_block(nf, nc, stride=2, padding=1, norm='no_norm', act=nn.Tanh()) elif (architecture == 'cnn_long'): first_map_shape = 3 gen_layers += linear_block(self.latent_dim, nf*4*first_map_shape*first_map_shape, norm='no_norm', act=nn.ReLU(True)) gen_layers += Reshape(-1, nf*4, first_map_shape, first_map_shape), gen_layers += convT_block(nf*4, nf*2, stride=2, padding=1, norm=self.norm, act=nn.ReLU(True)) gen_layers += convT_block(nf*2, nf, stride=2, padding=0, norm=self.norm, act=nn.ReLU(True)) gen_layers += convT_block(nf, nc, stride=2, padding=1, norm='no_norm', act=nn.Tanh()) else: raise ValueError('Architecture {} not implemented!'.format(architecture)) self.generate = nn.Sequential(*gen_layers) if print_shapes: input_tensor = torch.zeros(100,self.latent_dim) output = input_tensor print("\nGenerator ConvT Shapes:\n") for i, ly in enumerate(self.generate): output = self.generate[i](output) if (type(ly) == torch.nn.modules.conv.ConvTranspose2d): print('layer: {}'.format(i)) print(ly) print('output shape: {}'.format(output.shape)) def forward(self, z): img = self.generate(z) if self.architecture == 'mlp': img = img.view(-1,self.nc, self.img_size, self.img_size) return img class EncoderLayers(nn.Module): def __init__(self, architecture='cnn', nf=128, kernel_size=5, norm = 'no_norm', nc = 3, print_shapes=True ): super(EncoderLayers, self).__init__() print_shapes = False architecture_list = ['cnn', 'cnn_short', 'cnn_long'] normalization_list = ['layer_norm', 'spectral_norm', 'no_norm'] verify_string_args(architecture, architecture_list) verify_string_args(norm, normalization_list) self.img_size = 32 self.architecture = architecture self.nf = nf self.kernel_size = kernel_size self.norm = norm self.nc = nc self.leaky_relu = nn.LeakyReLU(0.2, inplace=True) #print('Normalization for conv layers is {}'.format(norm)) encoder_layers = [] if (architecture == 'cnn' or architecture == 'cnn_short'): encoder_layers += conv_block(nc, nf, fmap_shape=[16, 16], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) encoder_layers += conv_block(nf, nf * 2, fmap_shape=[8, 8], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) encoder_layers += conv_block(nf * 2, nf * 4, fmap_shape=[4,4], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) else: print('Architecture {} not implemented!'.format(architecture)) self.encoder_layers = nn.Sequential(*encoder_layers) if print_shapes: print("\nConv Features Shapes\n") input_tensor = torch.zeros(100, self.nc, self.img_size, self.img_size) output=input_tensor if architecture == 'mlp': output = input_tensor.view(100,-1) for i, ly in enumerate(self.encoder_layers): output = self.encoder_layers[i](output) if (type(ly) == torch.nn.modules.conv.Conv2d and print_shapes): print('layer: {}'.format(i)) print(ly) print('output shape: {}'.format(output.shape)) self.total_units = output.view(input_tensor.shape[0], -1).shape[-1] def forward(self, img): img_input_dim = img.shape[-1] if img_input_dim!=self.img_size: raise Exception("This discriminator/classifier assumes image inputs with {} resolution and an input with {} resolution was received. Please choose a compatible model or data.".format(self.img_size, img_input_dim)) if self.architecture == 'mlp': img = img.view(img.shape[0],-1) return self.encoder_layers(img)

py

HC-MGAN

HC-MGAN-main/models/models_general.py

import torch.nn as nn import torch.nn.functional as F import torch class GeneratorSet(nn.Module): def __init__(self, *gens): super(GeneratorSet, self).__init__() modules = nn.ModuleList() for gen in gens: modules.append(gen) self.paths = modules def forward(self, z, rand_perm=False): img = [] for path in self.paths: img.append(path(z)) img = torch.cat(img, dim=0) if rand_perm: img = img[torch.randperm(img.shape[0])] return img class Classifier(nn.Module): def __init__(self, feature_layers, no_c_outputs = 2, dropout = 0 ): super(Classifier, self).__init__() self.feature_layers = feature_layers self.no_c_outputs = no_c_outputs total_units = feature_layers.total_units self.linear_clasf = nn.Linear(total_units, no_c_outputs) self.dropout = nn.Dropout(dropout) self.log_softmax = nn.LogSoftmax(dim=1) def forward(self, input_tensor, feature_input = False): if feature_input: conv_features = input_tensor else: conv_features = (self.feature_layers(input_tensor)) conv_features = conv_features.view(conv_features.shape[0], -1) classification = self.dropout(conv_features) classification = self.linear_clasf(classification) classification = self.log_softmax(classification) return classification class Discriminator(nn.Module): def __init__(self, feature_layers ): super(Discriminator, self).__init__() self.feature_layers = feature_layers total_units = feature_layers.total_units self.linear_disc = nn.Linear(total_units, 1) def forward(self, input_tensor, feature_input = False): if feature_input: conv_features = input_tensor else: conv_features = (self.feature_layers(input_tensor)) conv_features = conv_features.view(conv_features.shape[0], -1) validity = self.linear_disc(conv_features) return validity

py

HC-MGAN

HC-MGAN-main/models/utils.py

from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torch def get_seq_model_shapes(seq_model, input_shape, seq_model_name = 'seq_model'): input_tensor = torch.zeros(*input_shape) output = input_tensor print("\n{} Layers:\n".format(seq_model_name)) for i, ly in enumerate(seq_model): output = seq_model[i](output) print('Layer Block {}: {}, out shape: {}'.format(i, ly, output.shape)) return output def verify_string_args(string_arg, string_args_list): if string_arg not in string_args_list: raise ValueError("Argument '{}' not available in {}".format(string_arg, string_args_list)) class Reshape(nn.Module): def __init__(self, *args): super(Reshape, self).__init__() self.shape = args def forward(self, x): return x.view(self.shape) def convT_block(nf_in, nf_out, stride = 2, padding = 1,norm='no_norm', act=None, kernel_size=4): block = [nn.ConvTranspose2d(nf_in, nf_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True )] if act is not None: block.append(act) return block def conv_block(nf_in, nf_out, stride = 2, padding = 2, fmap_shape=[10,10], norm=None, act=None, kernel_size=5): block = [nn.Conv2d(nf_in, nf_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True )] if norm == 'layer_norm': block.append(nn.LayerNorm([nf_out]+fmap_shape)) elif norm == 'spectral_norm': block[-1] = torch.nn.utils.spectral_norm(block[-1]) if act is not None: block.append(act) #block.append(nn.LeakyReLU(0.2, inplace=True)) #block.append(GaussianNoise(normal_std_scale=0.7)) return block def linear_block(nf_in, nf_out, norm='no_norm', act=None): block = [nn.Linear(nf_in, nf_out)] if norm == 'layer_norm': block.append(nn.LayerNorm([nf_out])) elif norm == 'spectral_norm': block[-1] = torch.nn.utils.spectral_norm(block[-1]) if act is not None: block.append(act) return block

py

HC-MGAN

HC-MGAN-main/models/gan.py

#torch imports from torch.autograd import Variable import torch import numpy as np class GAN: def __init__(self, gen_set, disc, clasf, feature_layers, optimizer_G, optimizer_D, optimizer_C, diversity_parameter_g ): '''Class for coordinating batch-wise update between the components of MGAN (raw split) or a GAN group (refinement) gen_set (torch.nn.Module): generator(s) disc (torch.nn.Module): discriminator clasf (torch.nn.Module): classifier feature_layers (torch.nn.Module): shared feature extractor for classifier and discriminator optimizer_G (torch.optim.Adam): Adam optimizer for generator(s) optimizer_D (torch.optim.Adam): Adam optimizer for discriminator optimizer_C (torch.optim.Adam): Adam optimizer for classifier diversity_parameter_g (float): hyperparameter for weighting generators' classification loss component ''' #components self.gen_set = gen_set self.disc = disc self.clasf = clasf self.feature_layers = feature_layers self.latent_dim = gen_set.paths[0].latent_dim self.diversity_parameter_g = diversity_parameter_g #optimizers self.optimizer_G = optimizer_G self.optimizer_D = optimizer_D self.optimizer_C = optimizer_C #losses self.loss_disc = torch.nn.BCEWithLogitsLoss() self.loss_clasf = torch.nn.NLLLoss() self.amp_enable = False self.metrics_dict = {'loss_disc_real': 0, 'acc_disc_real' : 0, 'loss_disc_fake': 0, 'acc_disc_fake': 0, 'loss_gen_disc': 0, 'loss_gen_clasf': 0, 'loss_clasf': 0, 'acc_clasf' : 0, } self.Tensor = torch.cuda.FloatTensor def bin_accuracy(self, pred, labels): corrects = (labels == torch.sigmoid(pred).round()).detach() acc = corrects.sum()/len(corrects) return acc def categorical_accuracy(self, pred, labels): corrects = (labels == torch.argmax(pred, dim = -1)).detach() acc = corrects.sum()/len(corrects) return acc def assign_amp(self, amp_autocast, amp_scaler): self.amp_autocast = amp_autocast self.amp_scaler = amp_scaler def enable_amp(self, amp_enable): self.amp_enable = amp_enable def train_on_batch(self, imgs_real, imgs_gen): '''Performs one iteration of update for Discriminator, Generators and Classifier (Raw Split training) imgs_real (torch.cuda.FloatTensor): mini-batch of real dataset images imgs_gen (torch.cuda.FloatTensor): mini-batch of generated images ''' self.gen_set.train() self.disc.train() #classification labels labels_c = [] labels_c.append(self.Tensor([0]*(imgs_gen.shape[0]//2))) labels_c.append(self.Tensor([1]*(imgs_gen.shape[0]//2))) labels_c = torch.cat(labels_c, dim=0).type(torch.cuda.LongTensor) #adversarial game labels labels_d_valid = Variable(self.Tensor(imgs_real.shape[0], 1).fill_(1.0), requires_grad=False) labels_d_fake = Variable(self.Tensor(imgs_gen.shape[0], 1).fill_(0.0), requires_grad=False) labels_g_valid = Variable(self.Tensor(imgs_gen.shape[0], 1).fill_(1.0), requires_grad=False) # -------------------- # Train Discriminator # -------------------- self.optimizer_D.zero_grad() with self.amp_autocast(self.amp_enable): #gets real images loss/acc validity = self.disc(imgs_real) loss_disc_real = self.loss_disc(validity, labels_d_valid) acc_disc_real = self.bin_accuracy(validity, labels_d_valid) #gets generated images loss/acc validity = self.disc(imgs_gen.detach()) loss_disc_fake = self.loss_disc(validity, labels_d_fake) acc_disc_fake = self.bin_accuracy(validity, labels_d_fake) #gets total loss for discriminator loss_disc = loss_disc_fake + loss_disc_real self.amp_scaler.scale(loss_disc).backward() self.amp_scaler.step(self.optimizer_D) # ----------------- # Train Classifier # ----------------- self.optimizer_C.zero_grad() with self.amp_autocast(self.amp_enable): for par in self.feature_layers.parameters(): par.requires_grad_(False) #gets classification loss/acc classification = self.clasf(imgs_gen.detach()) loss_clasf = self.loss_clasf(classification, labels_c) acc_clasf = self.categorical_accuracy(classification, labels_c) for par in self.feature_layers.parameters(): par.requires_grad_(True) self.amp_scaler.scale(loss_clasf).backward() self.amp_scaler.step(self.optimizer_C) # ----------------- # Train Generators # ----------------- self.optimizer_G.zero_grad() with self.amp_autocast(self.amp_enable): #gets discriminative loss/acc imgs_ft_gen = self.feature_layers(imgs_gen) validity = self.disc(imgs_ft_gen, feature_input=True) loss_gen_disc = self.loss_disc(validity, labels_g_valid) #gets classification loss/acc classification = self.clasf(imgs_ft_gen, feature_input=True) if self.diversity_parameter_g > 0: loss_gen_clasf = self.loss_clasf(classification, labels_c)*self.diversity_parameter_g #gets total loss for generators loss_gen = loss_gen_disc + loss_gen_clasf*self.diversity_parameter_g self.amp_scaler.scale(loss_gen).backward() self.amp_scaler.step(self.optimizer_G) #updates metrics dictionaries self.metrics_dict['loss_disc_real'] = loss_disc_real.item() self.metrics_dict['acc_disc_real'] = acc_disc_real.item() self.metrics_dict['loss_disc_fake'] = loss_disc_fake.item() self.metrics_dict['acc_disc_fake'] = acc_disc_fake.item() self.metrics_dict['loss_gen_disc'] = loss_gen_disc.item() self.metrics_dict['loss_gen_clasf'] = loss_gen_clasf.item() self.metrics_dict['loss_clasf'] = loss_clasf.item() self.metrics_dict['acc_clasf'] = acc_clasf.item() return self.metrics_dict def train_on_batch_refinement(self, imgs_real, imgs_gen_internal, imgs_gen_external=None, clasf_external=None): '''Performs one iteration of update for internal discriminator, internal generator, and internal classifier, also requiring external generator's data and external classifier (Refinement training) imgs_real (torch.cuda.FloatTensor): mini-batch of real dataset images imgs_gen_internal (torch.cuda.FloatTensor): mini-batch of generated images by the internal generator imgs_gen_external (torch.cuda.FloatTensor): mini-batch of generated images by the external generator for internal classifier's training clasf_external (torch.nn.Module): external classifier used by internal generator's training ''' self.gen_set.train() self.disc.train() #classification labels labels_c = [] labels_c.append(self.Tensor([0]*imgs_gen_internal.shape[0])) labels_c.append(self.Tensor([1]*imgs_gen_external.shape[0])) labels_c = torch.cat(labels_c, dim=0).type(torch.cuda.LongTensor) #adversarial labels labels_d_valid = Variable(self.Tensor(imgs_real.shape[0], 1).fill_(1.0), requires_grad=False) labels_d_fake = Variable(self.Tensor(imgs_gen_internal.shape[0], 1).fill_(0.0), requires_grad=False) labels_g_valid = Variable(self.Tensor(imgs_gen_internal.shape[0], 1).fill_(1.0), requires_grad=False) # -------------------- # Train Discriminator # -------------------- loss_disc_fake = self.Tensor([0]) loss_disc_real = self.Tensor([0]) acc_disc_real = self.Tensor([0]) acc_disc_fake = self.Tensor([0]) self.optimizer_D.zero_grad() with self.amp_autocast(self.amp_enable): #real images result validity = self.disc(imgs_real) loss_disc_real = self.loss_disc(validity, labels_d_valid) acc_disc_real = self.bin_accuracy(validity, labels_d_valid) #gen images result validity = self.disc(imgs_gen_internal.detach()) loss_disc_fake = self.loss_disc(validity, labels_d_fake) acc_disc_fake = self.bin_accuracy(validity, labels_d_fake) #total loss loss_disc = loss_disc_fake + loss_disc_real self.amp_scaler.scale(loss_disc).backward() self.amp_scaler.step(self.optimizer_D) # ----------------- # Train Classifier # ----------------- self.optimizer_C.zero_grad() with self.amp_autocast(self.amp_enable): for par in self.feature_layers.parameters(): par.requires_grad_(False) #gets classification classification_internal = self.clasf(imgs_gen_internal.detach()) classification_external = self.clasf(imgs_gen_external.detach()) classification_concat = torch.cat([classification_internal, classification_external]) #gets loss/acc loss_clasf = self.loss_clasf(classification_concat, labels_c) acc_clasf = self.categorical_accuracy(classification_concat, labels_c) for par in self.feature_layers.parameters(): par.requires_grad_(True) self.amp_scaler.scale(loss_clasf).backward() self.amp_scaler.step(self.optimizer_C) # ----------------- # Train Generators # ----------------- loss_gen_disc = self.Tensor([0]) loss_gen_clasf = self.Tensor([0]) self.optimizer_G.zero_grad() with self.amp_autocast(self.amp_enable): #gets discriminative loss/acc imgs_ft_gen_internal = self.feature_layers(imgs_gen_internal) validity = self.disc(imgs_ft_gen_internal, feature_input=True) loss_gen_disc = self.loss_disc(validity, labels_g_valid) #gets discriminative loss/acc classification_internal = self.clasf(imgs_ft_gen_internal, feature_input=True) if clasf_external.feature_layers == self.clasf.feature_layers: classification_external = clasf_external(imgs_ft_gen_internal, feature_input=True) else: classification_external = clasf_external(imgs_gen_internal) classification_concat = torch.cat([classification_internal, classification_external] ) if self.diversity_parameter_g > 0: loss_gen_clasf = self.loss_clasf(classification_concat, labels_c)*self.diversity_parameter_g loss_gen = loss_gen_disc + loss_gen_clasf self.amp_scaler.scale(loss_gen).backward() self.amp_scaler.step(self.optimizer_G) self.metrics_dict['loss_disc_real'] = loss_disc_real.item() self.metrics_dict['acc_disc_real'] = acc_disc_real.item() self.metrics_dict['loss_disc_fake'] = loss_disc_fake.item() self.metrics_dict['acc_disc_fake'] = acc_disc_fake.item() self.metrics_dict['loss_gen_disc'] = loss_gen_disc.item() self.metrics_dict['loss_gen_clasf'] = loss_gen_clasf.item() self.metrics_dict['loss_clasf'] = loss_clasf.item() self.metrics_dict['acc_clasf'] = acc_clasf.item() return self.metrics_dict def get_gen_images(self, z, rand_perm=False): return(self.gen_set(z, rand_perm=rand_perm)) def get_disc_losses_for_gen(self, imgs_gen_internal, no_generators=2): self.disc.train() batch_size = imgs_gen_internal.shape[0]//no_generators fake = Variable(self.Tensor(batch_size, 1).fill_(0.0), requires_grad=False) losses_for_gen = [] for i in range(no_generators): imgs_gen_i = imgs_gen_internal[batch_size*i:batch_size*(i+1)] with torch.no_grad(): validity = self.disc(imgs_gen_i.detach()) loss_fake = self.loss_disc(validity, fake).detach() losses_for_gen.append(loss_fake.item()) return losses_for_gen

py

HC-MGAN

HC-MGAN-main/models/models_28x28.py

import argparse import os from torch.autograd import Variable import torch.nn as nn import torch from models.utils import verify_string_args, linear_block, Reshape, convT_block, conv_block class Generator(nn.Module): def __init__(self, architecture = 'cnn', nf=128, kernel_size=4, latent_dim = 100, nc = 1, print_shapes=False, norm = 'no_norm' ): super(Generator, self).__init__() print_shapes = False architecture_list = ['cnn', 'cnn_short'] normalization_list = ['no_norm'] verify_string_args(architecture, architecture_list) verify_string_args(norm, normalization_list) self.img_size = 28 self.architecture = architecture self.nf = nf self.kernel_size = kernel_size self.latent_dim = latent_dim self.nc = nc self.norm = norm #print('Generator normalization is ', self.norm) gen_layers = [] if architecture == 'cnn' or architecture == 'cnn_short': first_map_shape = 7 gen_layers += linear_block(self.latent_dim, nf*2*first_map_shape*first_map_shape, norm='no_norm', act=nn.ReLU(True)) gen_layers += Reshape(-1, nf*2, first_map_shape, first_map_shape), gen_layers += convT_block(nf*2, nf, stride=2, padding=1, norm=self.norm, act=nn.ReLU(True)) gen_layers += convT_block(nf, nc, stride=2, padding=1, norm='no_norm', act=nn.Tanh()) else: print('Architecture {} not implemented!'.format(architecture)) self.generate = nn.Sequential(*gen_layers) if print_shapes: input_tensor = torch.zeros(100,self.latent_dim) output = input_tensor print("\nGenerator ConvT Shapes:\n") for i, ly in enumerate(self.generate): output = self.generate[i](output) if (type(ly) == torch.nn.modules.conv.ConvTranspose2d): print('layer: {}'.format(i)) print(ly) print('output shape: {}'.format(output.shape)) def forward(self, z): img = self.generate(z) if self.architecture == 'mlp': img = img.view(-1,self.nc, self.img_size, self.img_size) return img class EncoderLayers(nn.Module): def __init__(self, architecture='cnn', nf=128, kernel_size=5, norm = 'no_norm', nc = 1, print_shapes=True ): super(EncoderLayers, self).__init__() print_shapes = False architecture_list = ['cnn', 'cnn_short'] normalization_list = ['layer_norm', 'spectral_norm', 'no_norm'] verify_string_args(architecture, architecture_list) verify_string_args(norm, normalization_list) self.img_size = 28 self.architecture = architecture self.nf = nf self.kernel_size = kernel_size self.norm = norm self.nc = nc self.leaky_relu = nn.LeakyReLU(0.2, inplace=True) #print('Normalization for conv layers is {}'.format(norm)) encoder_layers = [] if (architecture == 'cnn' or architecture == 'cnn_short'): encoder_layers += conv_block(nc, nf, fmap_shape=[14, 14], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) encoder_layers += conv_block(nf, nf * 2, fmap_shape=[7, 7], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) encoder_layers += conv_block(nf * 2, nf * 4, fmap_shape=[4,4], norm=self.norm, act=self.leaky_relu, kernel_size=self.kernel_size) else: print('Architecture {} not implemented!'.format(architecture)) self.encoder_layers = nn.Sequential(*encoder_layers) if print_shapes: print("\nConv Features Shapes\n") input_tensor = torch.zeros(100, self.nc, self.img_size, self.img_size) output=input_tensor if architecture == 'mlp': output = input_tensor.view(100,-1) for i, ly in enumerate(self.encoder_layers): output = self.encoder_layers[i](output) if (type(ly) == torch.nn.modules.conv.Conv2d and print_shapes): print('layer: {}'.format(i)) print(ly) print('output shape: {}'.format(output.shape)) self.total_units = output.view(input_tensor.shape[0], -1).shape[-1] def forward(self, img): img_input_dim = img.shape[-1] if img_input_dim!=self.img_size: raise Exception("This discriminator/classifier assumes image inputs with {} resolution and an input with {} resolution was received. Please choose a compatible model or data.".format(self.img_size, img_input_dim)) if self.architecture == 'mlp': img = img.view(img.shape[0],-1) return self.encoder_layers(img)

py

HC-MGAN

HC-MGAN-main/tree/tree.py

import torch from tree.refinement import refinement from tree.raw_split import raw_split import numpy as np import copy import os from utils.soft_cluster import view_global_tree_logs, show, view_global_tree_logs from utils.others import save_log_text, remove_bold_from_string, print_save_log, get_log_heading class Node: def __init__(self, name, cluster_probs, tree_path, parent=None, child_left=None, child_right=None, node_status = "root node"): self.name = name self.cluster_probs = [cluster_probs] self.parent = parent self.child_left = child_left self.child_right = child_right self.tree_path = tree_path self.node_path = os.path.join(tree_path, name) self.status = node_status self.skipped_refinemnts = False def add_cluster_probs(self, cluster_probs): self.cluster_probs.append(cluster_probs) def create_children(self, cluster_probs_left, cluster_probs_right): self.child_left = Node(self.name + 'L', torch.Tensor(cluster_probs_left), tree_path=self.tree_path, parent=self, node_status='raw split') self.child_right = Node(self.name + 'R', torch.Tensor(cluster_probs_right), tree_path=self.tree_path, parent=self, node_status='raw split') def get_leaf_nodes_list(root_node): leaf_nodes_list = [] if (root_node.child_left is None) and (root_node.child_right is None): return [root_node] else: if root_node.child_left is not None: leaf_nodes_list += (get_leaf_nodes_list(root_node.child_left)) if root_node.child_right is not None: leaf_nodes_list += (get_leaf_nodes_list(root_node.child_right)) return leaf_nodes_list def get_non_leaf_nodes_list(root_node): if (root_node.child_left is None) and (root_node.child_right is None): return [] else: non_leaf_nodes_list = [root_node] if root_node.child_left is not None: non_leaf_nodes_list += (get_non_leaf_nodes_list(root_node.child_left)) if root_node.child_right is not None: non_leaf_nodes_list += (get_non_leaf_nodes_list(root_node.child_right)) return non_leaf_nodes_list def get_node_by_name(root_node, name): leaf_nodes_list = get_leaf_nodes_list(root_node) non_leaf_nodes_list = get_non_leaf_nodes_list(root_node) for node in leaf_nodes_list: if node.name == name: print("Node '{}' was found".format(name)) return node for node in non_leaf_nodes_list: if node.name == name: print("Node '{}' was found".format(name)) return node print("Node '{}' was not found".format(name)) return None def search_node_to_split(root_node, text_logs_path): log_headings = get_log_heading("SEARCHING NEXT LEAF NODE TO SPLIT", spacing=2) print_save_log('\n\n\n' + log_headings, text_logs_path) leaf_nodes_list = get_leaf_nodes_list(root_node) prob_mass_per_leaf = [leaf.cluster_probs[-1].sum() for leaf in leaf_nodes_list] split_node = leaf_nodes_list[np.argmax(prob_mass_per_leaf)] print_save_log('Currently {} leaf nodes obtained: '.format(len(leaf_nodes_list)), text_logs_path) print_save_log([(node.name, '{} prob. mass'.format(node.cluster_probs[-1].sum())) for node in leaf_nodes_list], text_logs_path) log = 'Selecting for split leaf node {} (prob. mass {}) following the greatest prob. mass criteria.'.format(split_node.name, split_node.cluster_probs[-1].sum()) print_save_log(log, text_logs_path) return split_node def raw_split_tree_node(args, node_k, dataloader_train, halt_epoch= 20, collapse_check_loss=0.01, save_node_path=None): dataloader_cluster_k = copy.deepcopy(dataloader_train) dataloader_cluster_k.sampler.weights = node_k.cluster_probs[-1] if node_k.node_path is not None: os.makedirs(node_k.node_path, exist_ok=True) trainer_raw_split = raw_split(args, dataloader_cluster_k, node_k, epochs=args.epochs_raw_split, noise_start= args.noise_start, sample_interval = args.sample_interval, collapse_check_loss=collapse_check_loss) #if save_node_path is not None: # np.save(save_node_path, node_k) return node_k def check_stop_refinement_condition(node, text_logs_path, min_prob_mass_variation = 150): if len(node.child_left.cluster_probs)>=3: prob_mass_variation = (node.child_left.cluster_probs[-1].numpy() - node.child_left.cluster_probs[-2].numpy()) prob_mass_variation = np.abs(prob_mass_variation).sum() log_headings = get_log_heading("CHECKING CONDITION FOR CONTINUING REFINEMENTS FOR NODE {}".format(node.name), spacing=2) print_save_log('\n\n\n' + log_headings, text_logs_path) print_save_log("Condition for continuing refinements: total prob mass variation between the last 2 refinements must be > {}.".format(min_prob_mass_variation), text_logs_path) print_save_log("(As a heuristic to save up time, we assume negligible variation indicates a clustering local minimum unlikely to change with more refinements)", text_logs_path) print_save_log('The variation of prob. mass for the last 2 refinemnets is: {:.2f}.'.format(prob_mass_variation), text_logs_path) if prob_mass_variation < min_prob_mass_variation: print_save_log('Canceling next refinements for this node.', text_logs_path) return True else: print_save_log('Continuing next refinements for this node. ', text_logs_path) return False else: return False def refine_tree_nodes(args, node_k, dataloader_train, ith_refinement, no_refinements, halt_epoch = 20, collapse_check_loss=0.01, save_node_path=None): ith_refinement = len(node_k.child_left.cluster_probs) dataloader_cluster_l = copy.deepcopy(dataloader_train) dataloader_cluster_m = copy.deepcopy(dataloader_train) dataloader_cluster_l.sampler.weights = node_k.child_left.cluster_probs[-1] dataloader_cluster_m.sampler.weights = node_k.child_right.cluster_probs[-1] trainer_ref= refinement(args, dataloader_cluster_l, dataloader_cluster_m, epochs=args.epochs_refinement, noise_start= args.noise_start, ref_it=ith_refinement, sample_interval=args.sample_interval, collapse_check_loss=collapse_check_loss, node_k = node_k, print_vars=False) dataloader_cluster_l.sampler.weights = node_k.child_left.cluster_probs[-1] dataloader_cluster_m.sampler.weights = node_k.child_right.cluster_probs[-1] #if save_node_path is not None: # np.save(save_node_path, node_k) return node_k def grow_tree_from_root(root_node, dataloader_train, args): os.makedirs(args.logs_path, exist_ok=True) text_logs_path = os.path.join(args.logs_path, "global_tree_logs.txt") save_log_text('', text_logs_path, open_mode='w') for i in range(args.no_splits): split_node = search_node_to_split(root_node, text_logs_path=text_logs_path) split_node = raw_split_tree_node(args, split_node, dataloader_train, save_node_path='root_node') non_leaf_list = get_non_leaf_nodes_list(root_node) leaf_list = get_leaf_nodes_list(root_node) log_title_raw_split = 'GLOBAL TREE LOGS AFTER RAW SPLIT OF NODE {}'.format(split_node.name) view_global_tree_logs(dataloader_train, non_leaf_list, leaf_list, text_logs_path, log_title=log_title_raw_split) for j in range(args.no_refinements): stop_refinement_flag = check_stop_refinement_condition(split_node, text_logs_path, args.min_prob_mass_variation) if not(stop_refinement_flag): split_node = refine_tree_nodes(args, split_node, dataloader_train, ith_refinement=j, no_refinements=args.no_refinements, save_node_path='root_node') if j == args.no_refinements-1: log_headings = get_log_heading("END OF RIFENEMENTS FOR NODE {} SPLIT".format(split_node.name), spacing=2) print_save_log("\n\n\n" + log_headings, text_logs_path) print_save_log("{}/{} refinements concluded.".format(args.no_refinements, args.no_refinements), text_logs_path) non_leaf_list = get_non_leaf_nodes_list(root_node) leaf_list = get_leaf_nodes_list(root_node) log_title_ref = 'GLOBAL TREE LOGS AFTER REFINEMENT {} OF NODE {} SPLIT'.format(j+1, split_node.name) view_global_tree_logs(dataloader_train, non_leaf_list, leaf_list, text_logs_path, log_title=log_title_ref) else: split_node.child_left.status += ", skipped {}".format(args.no_refinements-j) split_node.child_right.status += ", skipped {}".format(args.no_refinements-j) #np.save('root_node', root_node) log_headings = get_log_heading("END OF RIFINEMENTS FOR NODE {} SPLIT".format(split_node.name), spacing=2) print_save_log("\n\n\n" + log_headings, text_logs_path) print_save_log("{}/{} refinements concluded. Remaining {} refinements skipped due to negligible variation.".format( j, args.no_refinements, args.no_refinements-j), text_logs_path) break

py

HC-MGAN

HC-MGAN-main/tree/refinement.py

#basic imports import argparse import os import numpy as np import math import shutil import time import datetime import copy import sys #torch imports import torchvision.transforms as transforms from torchvision.utils import save_image, make_grid from torch.utils.data import DataLoader from torchvision import datasets from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torchvision import torch #plot imports import pandas as pd import matplotlib.pyplot as plt #from tabulate import tabulate #other imports from tqdm import autonotebook from sklearn import metrics #custom imports from utils.soft_cluster import get_classification_table_variation from utils.soft_cluster import show, distribution_select from utils.others import create_gans, sum_dicts, zero_dict_values, save_log_text, get_log_heading, get_bold_string, print_save_log try: from IPython.display import Image except: print('Jupyter image display not available') class GANGroupsTrainer: def __init__(self, dataloader_l, #dataloader object dataloader_m, #dataloader object gan_l, #gan object gan_m, #gan object amp_enable, #bool prior_distribution = 'uniform', node_k = None ): self.dl_l = dataloader_l self.dl_m = dataloader_m self.gan_l = gan_l self.gan_m = gan_m self.no_c_outputs = 2 self.amp_scaler = torch.cuda.amp.GradScaler() self.amp_autocast = torch.cuda.amp.autocast self.amp_enable = amp_enable self.cancel_training_flag = False self.class_to_idx_dict = dataloader_l.dataset.class_to_idx.items() self.idx_to_class_dict = {v:k for k,v in self.class_to_idx_dict} self.prior_distribution = prior_distribution self.node_k = node_k self.classifiers = [self.gan_l.clasf, self.gan_m.clasf] self.refresh_clustering_attributes() self.Tensor = torch.cuda.FloatTensor self.node_l = self.node_k.child_left self.node_m = self.node_k.child_right batch_size = dataloader_l.batch_size self.no_batches = int(dataloader_l.sampler.weights.sum().item() + dataloader_m.sampler.weights.sum().item())//(batch_size*2) self.gan_l.assign_amp(self.amp_autocast, self.amp_scaler) self.gan_l.enable_amp(self.amp_enable) self.gan_m.assign_amp(self.amp_autocast, self.amp_scaler) self.gan_m.enable_amp(self.amp_enable) def refresh_cluster_probs_per_example(self): self.cluster_probs_per_example = [self.dl_l.sampler.weights.numpy(), self.dl_m.sampler.weights.numpy()] def refresh_cluster_prob_mass_per_class(self): self.cluster_prob_mass_per_class = self.get_cluster_prob_mass_per_class( self.dl_l, self.cluster_probs_per_example) def refresh_cluster_assignments_per_class(self): self.cluster_assignments_per_class = self.get_cluster_assignments_per_class( self.dl_l, self.cluster_probs_per_example) def refresh_classes_for_monitoring(self): clusters_per_class_sum = [np.sum(clusters) for clusters in self.cluster_prob_mass_per_class] classes = self.dl_l.dataset.classes targets_per_example = self.dl_l.dataset.targets examples_per_class_sum = [np.sum(np.array(targets_per_example)==i) for i in range(len(classes))] self.classes_for_monitoring = [i for (i, sum_value) in enumerate(clusters_per_class_sum) if sum_value > examples_per_class_sum[i] * 0.2] if len(self.classes_for_monitoring) < 2: print('Classes for metrics monitoring were set to {}, ' 'which is too few (2 or more classes required)'.format( self.classes_for_monitoring)) print('This means clusters are too small ' '(prob. mass for classes < 20% of original mass at root node).') print('Enabling all classes for metrics monitoring.') self.classes_for_monitoring = np.arange(len(classes)).tolist() def refresh_clustering_attributes(self): self.refresh_cluster_probs_per_example() self.refresh_cluster_prob_mass_per_class() self.refresh_cluster_assignments_per_class() self.refresh_classes_for_monitoring() def train(self, epochs, text_logs_path, refinement_path, noise_start=0, sample_interval=20, collapse_check_loss=0.001, collapse_check_epoch=0, batch_size_gen=100, ref_it=0, ref_attempt=1, no_refinements=0): '''Main training loop. Args: epochs (int): total training epochs text_logs_path (string): .txt file to save textual training logs refinement_path (string): path to refinement folder where logs will be stored noise_start (float): Start image noise intensity linearly decaying throughout the training sample_interval (int): interval for sample logs printing/saving collapse_check_loss (float): threshold discriminator loss for detecting collapsed generators and halting the training batch_sige_gen (int): number of samples per minibatch for generated images ref_it (int): no. of iteration of refinement for printing/saving logs ref_attempt (int): no. of attempt for a given refinement it. (counts +1 if previous attempt was halted due to generators collapse) ''' self.refresh_clustering_attributes() self.cancel_training_flag = False print("\n\nTraining epochs progress bar (training logs printed/saved every {} epochs):".format(sample_interval)) for epoch in autonotebook.tqdm(range(1, epochs+1)): img_noise_scale = noise_start*(1-epoch/epochs) epoch_start = time.time() #running losses/acc dictionary epoch_metrics_dict_l = zero_dict_values(copy.copy(self.gan_l.metrics_dict)) epoch_metrics_dict_m = zero_dict_values(copy.copy(self.gan_m.metrics_dict)) dicts = self.train_on_epoch(epoch_metrics_dict_l, epoch_metrics_dict_m, img_noise_scale, batch_size_gen) epoch_metrics_dict_l, epoch_metrics_dict_m = dicts epoch_interval = time.time() - epoch_start #logs if (epoch % sample_interval) == 0: self.view_log_headings(epoch, epochs, epoch_interval, text_logs_path, ref_it=ref_it, ref_attempt=ref_attempt) self.view_epoch_losses(epoch_metrics_dict_l, epoch_metrics_dict_m, text_logs_path) self.view_gen_imgs(epoch, ref_attempt, refinement_path, text_logs_path) self.verify_collapsed_generators(epoch, text_logs_path, img_noise_scale, collapse_check_loss, collapse_check_epoch=collapse_check_epoch) #flag for cancelling training if generators collapses is detected if self.cancel_training_flag: break #prints end of training logs end_of_training_logs = "END OF REFINEMENT TRAINING FOR NODE {} SPLIT".format(self.node_k.name) print_save_log("\n\n"+get_log_heading(end_of_training_logs), text_logs_path) print_save_log("End of training.", text_logs_path) if not(self.cancel_training_flag): #gets cluster assignment probabilities for each example, avaraging the 2 classifiers results clasf_cluster_probs = self.get_clasf_cluster_probs(self.dl_l, self.classifiers, img_noise_scale) # updates children with new refined clasf cluster probs self.node_k.child_left.add_cluster_probs(torch.Tensor(clasf_cluster_probs[0])) self.node_k.child_left.status = "{}/{} refinements".format(ref_it, no_refinements) self.node_k.child_right.add_cluster_probs(torch.Tensor(clasf_cluster_probs[1])) self.node_k.child_right.status = "{}/{} refinements".format(ref_it, no_refinements) #end of training logs with refined binary clustering for current node new_cluster_prob_mass_per_class = self.get_cluster_prob_mass_per_class(self.dl_l, clasf_cluster_probs) self.view_new_clasf_clustering(new_cluster_prob_mass_per_class, ref_it, text_logs_path) def train_on_epoch(self, epoch_metrics_dict_l, epoch_metrics_dict_m, img_noise_scale, batch_size_gen=100): gan_l = self.gan_l gan_m = self.gan_m for batch_idx in range(self.no_batches): #samples real images from groups l and m imgs_real_l, imgs_real_m = self.get_real_images(img_noise_scale) #Trains group l components with needed external data/components from group m imgs_gen_l, imgs_gen_m = self.get_gen_images(img_noise_scale, batch_size_gen) batch_metrics_dict_l = gan_l.train_on_batch_refinement(imgs_real = imgs_real_l, imgs_gen_internal = imgs_gen_l, imgs_gen_external = imgs_gen_m, clasf_external = gan_m.clasf) epoch_metrics_dict_l = sum_dicts(epoch_metrics_dict_l, batch_metrics_dict_l) #Trains group m components with needed external data/components from group l imgs_gen_l, imgs_gen_m = self.get_gen_images(img_noise_scale, batch_size_gen) batch_metrics_dict_m = gan_m.train_on_batch_refinement(imgs_real = imgs_real_m, imgs_gen_internal = imgs_gen_m, imgs_gen_external = imgs_gen_l, clasf_external = gan_l.clasf) epoch_metrics_dict_m = sum_dicts(epoch_metrics_dict_m, batch_metrics_dict_m) #updates amp scaler after training components from groups l and m self.amp_scaler.update() return epoch_metrics_dict_l, epoch_metrics_dict_m def get_real_images(self, img_noise_scale): '''Gets real images from groups l and m''' imgs_real_l = next(iter(self.dl_l))[0].type(self.Tensor) imgs_real_l = self._add_noise(imgs_real_l, img_noise_scale) imgs_real_m = next(iter(self.dl_m))[0].type(self.Tensor) imgs_real_m = self._add_noise(imgs_real_m, img_noise_scale) return imgs_real_l, imgs_real_m def get_gen_images(self, img_noise_scale, batch_size=100): '''Generates imgs from each gan (already concatenated per gan)''' latent_dim = self.gan_l.latent_dim z = self.Tensor(distribution_select(self.prior_distribution, (batch_size, latent_dim))).requires_grad_(False) imgs_gen_l = self.gan_l.get_gen_images(z, rand_perm=True) imgs_gen_l = self._add_noise(imgs_gen_l, img_noise_scale) imgs_gen_m = self.gan_m.get_gen_images(z, rand_perm=True) imgs_gen_m = self._add_noise(imgs_gen_m, img_noise_scale) return imgs_gen_l, imgs_gen_m def get_cluster_prob_mass_per_class(self, dataloader, cluster_probs_per_example): no_of_clusters = 2 assert(len(cluster_probs_per_example) == no_of_clusters) no_of_classes = len(dataloader.dataset.classes) prob_mass_per_class = [] for i in range(no_of_classes): prob_mass_ij = [] for j in range(no_of_clusters): prob_mass_ij.append( ((np.array(dataloader.dataset.targets)==i)*cluster_probs_per_example[j]).sum().item() ) prob_mass_per_class.append(prob_mass_ij) return np.round(prob_mass_per_class, 2) def get_cluster_assignments_per_class(self, dataloader, cluster_probs_per_example): no_of_clusters = 2 assert(len(cluster_probs_per_example) == no_of_clusters) no_of_classes = len(dataloader.dataset.classes) cluster_assignments_per_class = [] for i in range(no_of_classes): cluster_counts_ij = [] for j in range(no_of_clusters): cluster_counts_ij.append( ((np.array(dataloader.dataset.targets)==i)*(cluster_probs_per_example[j])>0.5).sum().item() ) cluster_assignments_per_class.append(cluster_counts_ij) return cluster_assignments_per_class def get_clasf_cluster_probs(self, dataloader, classifiers, img_noise_scale=0): '''Performs cluster inference over the entire training set w/ the 2 classifiers. Returns the avg. cluster probabilities between the 2 classifiers for each training example.''' dataloader=torch.utils.data.DataLoader(dataloader.dataset, batch_size=100, shuffle=False, drop_last=False) #empty sublists to accumulate the minibatches of probabilities clasf_cluster_probs = [ [[] for _ in range(self.no_c_outputs)] for clasf in classifiers ] #iterates through the dataset to collect classifiers inference with minibatches for (batch_imgs, batch_targets) in dataloader: batch_imgs = batch_imgs.cuda() batch_imgs = self._add_noise(batch_imgs, img_noise_scale) with torch.no_grad(): clasf_cluster_probs_batch = [torch.exp(clasf(batch_imgs)).transpose(1,0) for clasf in classifiers] for i in range(len(classifiers)): for j in range(self.no_c_outputs): clasf_cluster_probs[i][j].append(clasf_cluster_probs_batch[i][j]) #concatenates results for each batch of the whole data clasf_cluster_probs = [[torch.cat(batch).cpu().numpy() for batch in classifier_i_batches] for classifier_i_batches in clasf_cluster_probs] #gets the average between the two classifiers' cluster probabilities clasf_cluster_probs_avg = np.array([(clasf_cluster_probs[0][0] + clasf_cluster_probs[1][1])/2, (clasf_cluster_probs[0][1] + clasf_cluster_probs[1][0])/2]) #gets parent node (k) probabilities by summing previous probabilities in l and m parent_cluster_probs = (self.dl_l.sampler.weights+self.dl_m.sampler.weights).numpy() #multiplies by the parent node`s probabilities clasf_cluster_probs_avg[0] *= parent_cluster_probs clasf_cluster_probs_avg[1] *= parent_cluster_probs clasf_cluster_probs_avg = clasf_cluster_probs_avg.tolist() return clasf_cluster_probs_avg def _plot_img_grid(self, imgs_plot, img_save_path, node_name, text_logs_path): if imgs_plot.shape[1] == 3 or imgs_plot.cpu().shape[1] == 1: grid = make_grid(imgs_plot, nrow=20, normalize=True) if img_save_path is not None: save_image(grid, img_save_path) try: print_save_log("\nSample of generated images from group {}:".format(node_name), text_logs_path) print_save_log('(This sample is saved at {})'.format(img_save_path), text_logs_path) display(Image(filename=img_save_path, width=900)) except: print_save_log('Jupyter image display not available for plotting sample of generated images from group {}'.format(node_name), text_logs_path) #print_save_log("Sample of generated images from group {} saved at {}".format(node_name, img_save_path), text_logs_path) else: print_save_log("\nNo image save path defined, can't save sample of generated images", text_logs_path) else: print_save_log("\nCan't plot/save imgs with shape {}".format(imgs_plot.shape), text_logs_path) def view_log_headings(self, epoch, epochs, epoch_interval, text_logs_path, ref_it=-1, ref_attempt=-1): '''Part 1/4 of training logs''' log_headings = "[REFINEMENT %d OF NODE %s SPLIT] [EPOCH %d/%d] [EPOCH TIME INTERVAL: %.2f sec.] [REF %d] [ATTEMPT %d]"%(ref_it, self.node_k.name, epoch, epochs, epoch_interval, ref_it, ref_attempt) log_headings = get_log_heading(log_headings) print_save_log("\n\n" + log_headings, text_logs_path) def view_epoch_losses(self, epoch_metrics_dict_l, epoch_metrics_dict_m, text_logs_path): '''part 2/4 of training logs''' print_save_log("Mean epoch losses/acc for each component in group l's GAN", text_logs_path) print_save_log({k:np.round(v/self.no_batches,5) for k,v in epoch_metrics_dict_l.items()}, text_logs_path) print_save_log("Mean epoch losses/acc for each component in group m's GAN:", text_logs_path) print_save_log({k:np.round(v/self.no_batches,5) for k,v in epoch_metrics_dict_m.items()}, text_logs_path) def view_gen_imgs(self, epoch, ref_attempt, refinement_path, text_logs_path): '''part 3/4 of training logs''' imgs_plot_l, imgs_plot_m = self.get_gen_images(img_noise_scale=0, batch_size=10) if self.node_k is not None: if refinement_path is not None: img_save_path_l = refinement_path + "attempt_{}_ep_{}_{}.jpg".format(ref_attempt, epoch, self.node_l.name) img_save_path_m = refinement_path + "attempt_{}_ep_{}_{}.jpg".format(ref_attempt, epoch, self.node_m.name) self._plot_img_grid(imgs_plot_l, img_save_path_l, "l", text_logs_path) self._plot_img_grid(imgs_plot_m, img_save_path_m, "m", text_logs_path) def verify_collapsed_generators(self, epoch, text_logs_path, img_noise_scale=0, collapse_check_loss=0.01, collapse_check_epoch=50, batch_size=100): '''part 4/4 of training logs''' if epoch < collapse_check_epoch: print_save_log("\nGenerator collapse will be checked after epoch {}".format(collapse_check_epoch), text_logs_path) else: print_save_log("\nChecking if generators have collapsed...", text_logs_path) imgs_gen_l, imgs_gen_m = self.get_gen_images(img_noise_scale, batch_size) losses_l = self.gan_l.get_disc_losses_for_gen(imgs_gen_l) losses_m = self.gan_m.get_disc_losses_for_gen(imgs_gen_m) for loss in losses_l + losses_m: if loss < collapse_check_loss and epoch>=collapse_check_epoch: log_string = "\nDiscriminator loss for generated images is too low (<{}), indicating generators collapse. The training shall restart.".format(collapse_check_loss) print_save_log(log_string, text_logs_path) self.cancel_training_flag = True break if not(self.cancel_training_flag): print_save_log("Generators collapse not found, the training shall continue.", text_logs_path) def view_new_clasf_clustering(self, new_cluster_prob_mass_per_class, ref_it, text_logs_path): '''Prints logs with refined binary clustering result for current nodes''' #header log_headings = "REFINED BINARY CLUSTERING FOR NODE {} SPLIT OBTAINED WITH AVG CLASSIFIER'S INFERENCE".format(self.node_k.name) log_headings = get_log_heading(log_headings) print_save_log("\n\n"+log_headings, text_logs_path) #clustering table print_save_log("Local binary soft clustering (prob. mass division) for node {} split after refinement, according to each class.".format(self.node_k.name), text_logs_path) print_save_log('Probability mass variation since last refinement or raw split is indicated in parenthesis for each cluster and class.', text_logs_path) print('(This table is saved at {})'.format(text_logs_path)) table_df = get_classification_table_variation(self.cluster_prob_mass_per_class, new_cluster_prob_mass_per_class, self.idx_to_class_dict, node=self.node_k, table_name = 'Local split soft clusters refined') pd.set_option("max_colwidth", None) pd.set_option('max_columns', None) try: display(table_df) except: print(table_df) print_save_log(str(table_df), text_logs_path, print_log=False) def _add_noise(self, tensor, normal_std_scale): if (normal_std_scale > 0): return tensor + (tensor*torch.randn_like(tensor)*normal_std_scale) else: return tensor def refinement(args, dataloader_l, dataloader_m, epochs, noise_start, ref_it = -1, sample_interval=10, collapse_check_loss=0.001, save=False, node_k=None, print_vars=False): redo_training = True ref_attempt = 1 max_attempts = 4 while redo_training: #configure log saving paths refinement_path = os.path.join(node_k.node_path, "refinement_{}/".format(ref_it)) os.makedirs(refinement_path, exist_ok=True) text_logs_path = refinement_path + "attempt_{}_training_logs.txt".format(ref_attempt) save_log_text('', text_logs_path, open_mode='w') #print main log headings log_headings = 'REFINEMENT {} OF OF NODE {} SPLIT (ATTEMPT {})'.format(ref_it, node_k.name, ref_attempt) log_headings = get_log_heading(log_headings, spacing=2) print_save_log('\n\n\n' + log_headings, text_logs_path) #print parameters log_headings = get_log_heading("TRAINING PARAMETERS") print_save_log(log_headings, text_logs_path) print_save_log("Training Arguments: ", text_logs_path) print_save_log(vars(args), text_logs_path) print_save_log("Training using device : {}".format(args.device), text_logs_path) print_save_log("Training logs save path: {}".format(text_logs_path), text_logs_path) print_save_log("Limit of Training Attempts: {}".format(max_attempts), text_logs_path) #create MGAN models [gan_l, gan_m] = create_gans(args, no_gans=2) #print models' architecture log_headings = get_log_heading("MODELS ARCHITETURE") print_save_log('\n\n' + log_headings, text_logs_path) print_save_log("Discriminator Architecture:", text_logs_path) print_save_log(gan_l.disc, text_logs_path) print_save_log("\nGernerator Architecture:", text_logs_path) print_save_log(gan_l.gen_set.paths[0], text_logs_path) print_save_log("\nClassifier Architecture:", text_logs_path) print_save_log(gan_l.clasf, text_logs_path) trainer = GANGroupsTrainer(dataloader_l, dataloader_m, gan_l, gan_m, amp_enable=args.amp_enable, prior_distribution = "uniform", node_k = node_k) trainer.train(epochs = epochs, text_logs_path=text_logs_path, refinement_path = refinement_path, noise_start=noise_start, collapse_check_loss=collapse_check_loss, collapse_check_epoch=args.collapse_check_epoch, sample_interval=sample_interval, ref_it=ref_it, batch_size_gen=args.batch_size_gen, ref_attempt = ref_attempt, no_refinements=args.no_refinements) #flag for restarting the training if generation collapse is detected if trainer.cancel_training_flag == False: redo_training = False else: ref_attempt += 1 if ref_attempt>max_attempts: max_attempt_log_headings = get_log_heading("LIMIT OF {} FAILED ATTEMPTS REACHED".format(max_attempts)) max_attempt_log = "The training for this refinement reached the limit of {} failed attempts due generation collapse.".format(max_attempts) max_attempt_log += " Please, select more stable tunnings for the models so that the generation stops collapsing." print_save_log("\n\n" + max_attempt_log_headings, text_logs_path) print_save_log(max_attempt_log, text_logs_path) sys.exit(max_attempt_log) return trainer

py

HC-MGAN

HC-MGAN-main/tree/raw_split.py

#basic imports import argparse import os import numpy as np import math import shutil import time import datetime import copy import sys #torch imports import torchvision.transforms as transforms from torchvision.utils import save_image, make_grid from torch.utils.data import DataLoader from torchvision import datasets from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torchvision import torch #plot imports #import seaborn as sn import pandas as pd import matplotlib.pyplot as plt #from tabulate import tabulate #other imports from tqdm import autonotebook from sklearn import metrics #import nbimporter #custom imports from utils.soft_cluster import get_local_cluster_table, show, distribution_select from utils.others import create_gans, sum_dicts, save_log_text, get_log_heading, print_save_log, zero_dict_values try: from IPython.display import Image except: print('Jupyter image display not available') class MGANTrainer: def __init__(self, dataloader_cluster_k, #dataloader object mgan_k, #mgan object amp_enable, #bool prior_distribution = 'uniform', node_k = None, feat_extractor = None ): self.dl_k = dataloader_cluster_k self.mgan_k = mgan_k self.latent_dim = mgan_k.latent_dim self.no_c_outputs = mgan_k.clasf.linear_clasf.out_features self.amp_scaler = torch.cuda.amp.GradScaler() self.amp_autocast = torch.cuda.amp.autocast self.amp_enable = amp_enable self.cancel_training_flag = False self.class_to_idx_dict = dataloader_cluster_k.dataset.class_to_idx.items() self.idx_to_class_dict = {v:k for k,v in self.class_to_idx_dict} self.classes_names_dict = {v:k for k,v in dataloader_cluster_k.dataset.class_to_idx.items()} self.classes_targets_groups = [ dataloader_cluster_k.dataset.class_to_idx[class_name] for class_name in dataloader_cluster_k.dataset.classes] self.prior_distribution = prior_distribution self.node_k = node_k self.feat_extractor = feat_extractor self.mgan_k.assign_amp(self.amp_autocast, self.amp_scaler) self.mgan_k.enable_amp(self.amp_enable) CUDA = True if torch.cuda.is_available() else False self.Tensor = torch.cuda.FloatTensor if CUDA else torch.FloatTensor self.no_batches = int(dataloader_cluster_k.sampler.weights.sum().item())//dataloader_cluster_k.batch_size def train(self, epochs, text_logs_path, raw_split_path, noise_start=0, sample_interval=20, collapse_check_loss=0.001, collapse_check_epoch=0, batch_size_gen=100, raw_split_attempt=0): '''Main training loop. Args: epochs (int): total training epochs text_logs_path (string): .txt file to save textual training logs raw_split_path (string): path to raw split folder where logs will be stored noise_start (float): Start image noise intensity linearly decaying throughout the training sample_interval (int): interval for sample logs printing/saving collapse_check_loss (float): threshold discriminator loss for detecting collapsed generators and halting the training batch_sige_gen (int): no. of samples per minibatch for generated images ref_it (int): no. of iteration of refinement for printing/saving logs ref_attempt (int): no. of attempt for a given refinement it. (counts +1 if previous attempt was halted due to generators collapse) ''' self.cancel_training_flag = False print("\n\nTraining epochs progress bar (training logs printed/saved every {} epochs):".format(sample_interval)) for epoch in autonotebook.tqdm(range(1, epochs+1)): img_noise_scale = noise_start*(1-epoch/epochs) epoch_start = time.time() #running losses/acc dictionary epoch_metrics_dict = zero_dict_values(copy.copy(self.mgan_k.metrics_dict)) epoch_metrics_dict = self.train_on_epoch(epoch_metrics_dict, img_noise_scale, batch_size_gen) epoch_interval = time.time() - epoch_start #logs if (epoch % sample_interval) == 0: #text_logs_path = self.raw_split_path + "attempt_{}_ep_{}_logs.txt".format(raw_split_attempt, epoch) self.view_log_headings(epoch, epochs, epoch_interval, text_logs_path, raw_split_attempt) self.view_epoch_losses(epoch_metrics_dict, text_logs_path) self.view_gen_imgs(epoch, raw_split_attempt, raw_split_path, text_logs_path) self.verify_collapsed_generators(epoch, text_logs_path, img_noise_scale, collapse_check_loss, collapse_check_epoch) #flag for cancelling the training if generators collapse is detected if self.cancel_training_flag: break #prints end of training logs print_save_log("\n\n"+get_log_heading("END OF RAW SPLIT TRAINING FOR NODE {}".format(self.node_k.name)), text_logs_path) print_save_log("End of training.", text_logs_path) if not(self.cancel_training_flag): #gets cluster assignment probabilities for each example with classifier's inference clasf_cluster_probs = self.get_clasf_cluster_probs(self.dl_k, self.mgan_k.clasf, img_noise_scale) # creates children with new clasf cluster probs self.node_k.create_children(cluster_probs_left = clasf_cluster_probs[0], cluster_probs_right = clasf_cluster_probs[1]) #logs with the binary clustering result for current node's raw split new_cluster_prob_mass_per_class = self.get_cluster_prob_mass_per_class(self.dl_k, clasf_cluster_probs) self.view_new_clasf_clustering(new_cluster_prob_mass_per_class, text_logs_path) def train_on_epoch(self, epoch_metrics_dict, img_noise_scale, batch_size_gen=100): mgan_k = self.mgan_k for batch_idx in range(self.no_batches): #samples real images from groups l and m imgs_real_k = self.get_real_images(img_noise_scale) #Trains group l components with needed external data/components from group m imgs_gen_k = self.get_gen_images(img_noise_scale, batch_size_gen) batch_metrics_dict = mgan_k.train_on_batch(imgs_real = imgs_real_k, imgs_gen = imgs_gen_k) epoch_metrics_dict = sum_dicts(epoch_metrics_dict, batch_metrics_dict) #updates amp scaler after training components from groups l and m self.amp_scaler.update() return epoch_metrics_dict def _get_classes_for_monitoring(self, cluster_prob_mass_per_class, min_proportion=0.2): clusters_per_class_sum = [np.sum(clusters) for clusters in cluster_prob_mass_per_class] classes = self.dl_k.dataset.classes targets_per_example = self.dl_k.dataset.targets examples_per_class_sum = [np.sum(np.array(targets_per_example)==i) for i in range(len(classes))] classes_for_monitoring = [i for (i, sum_value) in enumerate(clusters_per_class_sum) if sum_value > examples_per_class_sum[i] * min_proportion] if len(classes_for_monitoring) < 2: print('\nClasses for metrics monitoring were set to {}, ' 'which is too few (2 or more classes required)'.format(classes_for_monitoring)) print('This means clusters are too small (prob. mass for classes < {}} of original mass at root node).'.format(min_proportion)) print('Enabling all classes for metrics monitoring.') classes_for_monitoring = np.arange(len(classes)).tolist() return classes_for_monitoring else: return classes_for_monitoring def get_real_images(self, img_noise_scale): '''Gets real images from groups l and m''' imgs_real_k = next(iter(self.dl_k))[0].type(self.Tensor) imgs_real_k = self._add_noise(imgs_real_k, img_noise_scale) return imgs_real_k def get_clasf_cluster_probs(self, dataloader, classifier, img_noise_scale=0): '''Performs cluster inference over the entire training set w/ the 1 classifier.''' dataloader=torch.utils.data.DataLoader(dataloader.dataset, batch_size=100, shuffle=False, drop_last=False) #empty sublists to accumulate the minibatches of probabilities clasf_cluster_probs = [[] for _ in range(self.no_c_outputs)] #iterates through the dataset to collect classifiers inference with minibatches for (batch_imgs, batch_targets) in dataloader: batch_imgs = batch_imgs.cuda() batch_imgs = self._add_noise(batch_imgs, img_noise_scale) with torch.no_grad(): clasf_cluster_probs_batch = torch.exp(classifier(batch_imgs)).transpose(1,0) for i in range(self.no_c_outputs): clasf_cluster_probs[i].append(clasf_cluster_probs_batch[i]) #concatenates results for each batch of the whole data clasf_cluster_probs = np.array([torch.cat(batch).cpu().numpy() for batch in clasf_cluster_probs]) #gets parent node (k) probabilities by summing previous probabilities in l and m current_cluster_probs = self.dl_k.sampler.weights.numpy() #multiplies clasf inference by the current node`s probabilities clasf_cluster_probs[0] *= current_cluster_probs clasf_cluster_probs[1] *= current_cluster_probs return clasf_cluster_probs.tolist() def get_cluster_prob_mass_per_class(self, dataloader, cluster_probs_per_example): no_of_clusters = 2 assert(len(cluster_probs_per_example) == no_of_clusters) no_of_classes = len(dataloader.dataset.classes) prob_mass_per_class = [] for i in range(no_of_classes): prob_mass_ij = [] for j in range(no_of_clusters): prob_mass_ij.append( ((np.array(dataloader.dataset.targets)==i)*cluster_probs_per_example[j]).sum().item() ) prob_mass_per_class.append(prob_mass_ij) return np.round(prob_mass_per_class, 2) def get_cluster_assignments_per_class(self, dataloader, cluster_probs_per_example): no_of_clusters = 2 assert(len(cluster_probs_per_example) == no_of_clusters) no_of_classes = len(dataloader.dataset.classes) cluster_assignments_per_class = [] for i in range(no_of_classes): cluster_counts_ij = [] for j in range(no_of_clusters): cluster_counts_ij.append( ((np.array(dataloader.dataset.targets)==i)*(cluster_probs_per_example[j])>0.5).sum().item() ) cluster_assignments_per_class.append(cluster_counts_ij) return cluster_assignments_per_class def view_log_headings(self, epoch, epochs, epoch_interval, text_logs_path, raw_split_attempt=0): '''Part 1/4 of training logs''' log_headings = "[RAW SPLIT NODE %s] [EPOCH %d/%d] [EPOCH TIME INTERVAL: %.2f sec.] [ATTEMPT %d]"%(self.node_k.name, epoch, epochs, epoch_interval, raw_split_attempt) log_headings = get_log_heading(log_headings) print_save_log('\n\n' + log_headings, text_logs_path) def view_epoch_losses(self, epoch_metrics_dict, text_logs_path): '''Part 2/4 of training logs''' log_string = 'Mean epoch losses/acc for each component in the MGAN: \n' log_string += str({k:np.round(v/self.no_batches,5) for k,v in epoch_metrics_dict.items()}) + '\n' print_save_log(log_string, text_logs_path) def view_gen_imgs(self, epoch, raw_split_attempt, raw_split_path, text_logs_path): '''Part 3/4 of training logs''' imgs_plot = self.get_gen_images(img_noise_scale=0, batch_size=20) if self.node_k is not None: if raw_split_path is not None: img_save_path = raw_split_path + "attempt_{}_ep_{}.jpg".format(raw_split_attempt, epoch) self._plot_img_grid(imgs_plot, img_save_path, self.node_k.name, text_logs_path) def verify_collapsed_generators(self, epoch, text_logs_path, img_noise_scale=0, collapse_check_loss=0.01, collapse_check_epoch=50, batch_size=100): '''Part 4/4 of training logs''' if epoch < collapse_check_epoch: print_save_log("\nGenerator collapse will be checked after epoch {}".format(collapse_check_epoch), text_logs_path) else: imgs_gens_k = self.get_gen_images(img_noise_scale, batch_size) losses = self.mgan_k.get_disc_losses_for_gen(imgs_gens_k) for loss in losses: if loss < collapse_check_loss and epoch>=collapse_check_epoch: log_string = "\nDiscriminator loss for generated images is too low (<{}), indicating generators collapse. The training shall restart.".format(collapse_check_loss) print_save_log(log_string, text_logs_path) self.cancel_training_flag = True break if not(self.cancel_training_flag): print_save_log("\nGenerator collapse check: no collapse detected, training shall continue.", text_logs_path) else: print_save_log("\nGenerator collapse check: collapse detected, restart training.", text_logs_path) def view_new_clasf_clustering(self, new_cluster_prob_mass_per_class, text_logs_path): '''Prints logs with binary clustering result for current node''' #header log_headings = "EXHIBITING BINARY CLUSTERING FOR NODE %s OBTAINED WITH CLASSIFIER'S INFERENCE"%(self.node_k.name) log_headings = get_log_heading(log_headings) print_save_log("\n\n"+log_headings, text_logs_path) #clustering table log_text_1 = 'Local binary soft clustering (prob. mass division) for node {}, according to each reference class\n'.format(self.node_k.name) print_save_log(log_text_1, text_logs_path) table_df = get_local_cluster_table(new_cluster_prob_mass_per_class, self.idx_to_class_dict, node=self.node_k, table_name = 'Local soft clusters from binary split') pd.set_option("max_colwidth", None) pd.set_option('max_columns', None) try: display(table_df) except: print(table_df) print_save_log(str(table_df), text_logs_path, print_log=False) def _add_noise(self, tensor, normal_std_scale): if (normal_std_scale > 0): return tensor + (tensor*torch.randn_like(tensor)*normal_std_scale) else: return tensor def get_gen_images(self, img_noise_scale, batch_size=100): '''Generates imgs from each gan (already concatenated per gan)''' latent_dim = self.mgan_k.latent_dim z = self.Tensor(distribution_select(self.prior_distribution, (batch_size, latent_dim))).requires_grad_(False) imgs_gen = self.mgan_k.get_gen_images(z, rand_perm=False) imgs_gen = self._add_noise(imgs_gen, img_noise_scale) return imgs_gen def _plot_img_grid(self, imgs_plot, img_save_path, node_name, text_logs_path): if imgs_plot.shape[1] == 3 or imgs_plot.shape[1] == 1: grid = make_grid(imgs_plot.cpu(), nrow=20, normalize=True) if img_save_path is not None: save_image(grid, img_save_path) try: print_save_log("\nSample of generated images from raw split MGAN for node {} (each row for each generator' output):".format(node_name), text_logs_path) print_save_log('(This sample is saved at {})'.format(img_save_path), text_logs_path) display(Image(filename=img_save_path, width=900)) except: print_save_log('Jupyter image display not available for plotting sample of generated images', text_logs_path) #print_save_log("Sample of generated images (each row for each generator' output) saved at {}".format(img_save_path), text_logs_path) else: print_save_log("\nNo image save path defined, can't save sample of generated images", text_logs_path) else: print_save_log("\nCan't plot/save imgs with shape {}".format(imgs_plot.shape), text_logs_path) def raw_split(args, dataloader_cluster_k, node_k, epochs, noise_start, sample_interval=10, collapse_check_loss=0.001): restart_training = True raw_split_attempt = 1 max_attempts = 4 while restart_training: #configure log saving paths raw_split_path = os.path.join(node_k.node_path, "raw_split/") os.makedirs(raw_split_path, exist_ok=True) text_logs_path = raw_split_path + "attempt_{}_training_logs.txt".format(raw_split_attempt) save_log_text('', text_logs_path, open_mode='w') #print main log headings log_headings = get_log_heading("RAW SPLIT OF NODE {} (ATTEMPT {}) ".format(node_k.name, raw_split_attempt), spacing=2) print_save_log('\n\n\n' + log_headings, text_logs_path) #print parameters log_headings = get_log_heading("TRAINING PARAMETERS") print_save_log(log_headings, text_logs_path) print_save_log("Training Arguments: ", text_logs_path) print_save_log(vars(args), text_logs_path) print_save_log("Training using device : {}".format(args.device), text_logs_path) print_save_log("Training logs save path: {}".format(text_logs_path), text_logs_path) print_save_log("Limit of Training Attempts: {}".format(max_attempts), text_logs_path) #create MGAN models [mgan_k] = create_gans(args, no_gans=1, no_g_paths=2) #print models' architecture log_headings = get_log_heading("MODELS ARCHITETURE") print_save_log('\n\n' + log_headings, text_logs_path) print_save_log("Discriminator Architecture:", text_logs_path) print_save_log(mgan_k.disc, text_logs_path) print_save_log("\nGernerator Architecture:", text_logs_path) print_save_log(mgan_k.gen_set.paths[0], text_logs_path) print_save_log("\nClassifier Architecture:", text_logs_path) print_save_log(mgan_k.clasf, text_logs_path) #create trainer object trainer = MGANTrainer(dataloader_cluster_k, mgan_k, amp_enable=args.amp_enable, prior_distribution = "uniform", node_k = node_k ) #train trainer.train(epochs = epochs, text_logs_path = text_logs_path, raw_split_path = raw_split_path, noise_start=noise_start, sample_interval=sample_interval, collapse_check_loss =collapse_check_loss, collapse_check_epoch = args.collapse_check_epoch, raw_split_attempt = raw_split_attempt, ) #flag for restarting the training if generation collapse is detected if trainer.cancel_training_flag == False: restart_training = False else: raw_split_attempt += 1 if raw_split_attempt>max_attempts: max_attempt_log_headings = get_log_heading("LIMIT OF {} FAILED ATTEMPTS REACHED".format(max_attempts)) max_attempt_log = "The training for the raw split of node {} reached the limit of {} failed attempts due generation collapse.".format(node_k, max_attempts) max_attempt_log += " Please, select more stable tunnings for the models so that the generation stops collapsing." print_save_log("\n\n" + max_attempt_log_headings, text_logs_path) print_save_log(max_attempt_log, text_logs_path) sys.exit(max_attempt_log) return trainer

py

HC-MGAN

HC-MGAN-main/utils/others.py

import os import math import torch import torchvision.transforms as transforms from torchvision.utils import save_image, make_grid from torchvision import datasets import torch from models.gan import GAN def sum_dicts(dict_a, dict_b): assert(dict_a.keys() == dict_b.keys()) return {k:dict_a[k]+dict_b[k] for k,v in dict_a.items()} def zero_dict_values(dict): return {k:0 for k,v in dict.items()} def save_log_text(log_text, save_path, open_mode = 'a'): try: with open(save_path, open_mode) as f: f.write(log_text) except FileNotFoundError: print("Path {} for saving training logs does not exist".format(save_path)) def print_save_log(log, save_path, print_log=True): if print_log: print(log) if save_path is not None: save_log_text(remove_bold_from_string(str(log))+'\n', save_path, open_mode='a') def get_log_heading(text, spacing=0): hyphen_bar = (len(text)+2)*'-' line_break = ('#\n')*spacing return get_bold_string(hyphen_bar + '\n'+ line_break + '# ' + text + '\n'+ line_break + hyphen_bar) def get_bold_string(string): return "\033[1m" + string + "\033[0m" def remove_bold_from_string(string): string = string.replace('\033[1m', '') string = string.replace('\033[0m', '') return string def create_gans(args, no_gans=1, no_g_paths=2): available_img_dim = [28, 32] import models.models_general as mdg if args.img_dim == 32: import models.models_32x32 as md elif args.img_dim == 28: import models.models_28x28 as md else: raise ValueError('Data type {} not available, choose from {}'.format(args.data_type, available_img_dim)) def create_feature_layer(): return md.EncoderLayers(architecture=args.architecture_d, nf = args.nf_d, kernel_size=args.kernel_size_d, norm=args.normalization_d, nc=args.img_channels, print_shapes=True) gan_list = [] def create_gen(): return md.Generator(architecture = args.architecture_g, nf = args.nf_g, kernel_size = args.kernel_size_g, latent_dim = args.latent_dim, nc = args.img_channels, norm = args.normalization_g, print_shapes=True) if args.shared_features_across_ref: shared_feature_layers = create_feature_layer().cuda() for i in range(no_gans): gen = create_gen().cuda() gens = [gen] for i in range(no_g_paths-1): gens.append(create_gen().cuda()) gen_set = mdg.GeneratorSet(*gens) if args.shared_features_across_ref: feature_layers = shared_feature_layers else: feature_layers = create_feature_layer().cuda() disc = mdg.Discriminator(feature_layers).cuda() clasf = mdg.Classifier(feature_layers, no_c_outputs=2).cuda() #optimizers optimizer_G = torch.optim.Adam(list(gen_set.parameters()), lr=args.lr_g, betas=(args.b1, args.b2)) optimizer_D = torch.optim.Adam(list(disc.parameters()), lr=args.lr_d, betas=(args.b1, args.b2)) optimizer_C = torch.optim.Adam(list(clasf.linear_clasf.parameters()), lr=args.lr_c, betas=(args.b1, args.b2)) gan = GAN(gen_set, disc, clasf, feature_layers, optimizer_G, optimizer_D, optimizer_C, args.diversity_parameter_g) gan_list.append(gan) return gan_list '''def get_pretty_df(df): dfStyler = df.style.set_properties(**{'text-align': 'center', 'border' : '1px solid !important' }) df = dfStyler.set_table_styles([dict(selector='th', props=[('text-align', 'center')])]) return df'''

py

HC-MGAN

HC-MGAN-main/utils/data.py

import os import math import torch import torchvision.transforms as transforms from torchvision.utils import save_image, make_grid from torchvision import datasets from torch.utils.data import Dataset import torch class MyDataset(Dataset): def __init__(self, dataset): self.dataset = dataset self.targets = dataset.targets def __getitem__(self, index): data, target = self.dataset[index] return data, target, index def __len__(self): return len(self.dataset) def merge_dataloaders(dl1, dl2): dl1.dataset.data = torch.cat([dl1.dataset.data, dl2.dataset.data]) dl1.dataset.targets = torch.cat([dl1.dataset.targets, dl2.dataset.targets]) dl1.sampler.weights = torch.cat([dl1.sampler.weights, dl2.sampler.weights]) return dl1 def create_dataloader(dataset, test = False, batch_size = 100, path='../data/'): available_datasets = ['fmnist', 'mnist','sop'] if dataset not in available_datasets: raise ValueError('Dataset {} not available, choose from {}'.format(dataset, available_datasets)) os.makedirs(path, exist_ok=True) if dataset == 'fmnist' : if test: sampler = torch.utils.data.WeightedRandomSampler(weights=[1]*10000, num_samples=batch_size, replacement=True, generator=None) else: sampler = torch.utils.data.WeightedRandomSampler(weights=[1]*60000, num_samples=batch_size, replacement=True, generator=None) dataloader = torch.utils.data.DataLoader( datasets.FashionMNIST(path, train=not(test), download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize([.5],[.5],[.5]) ])), batch_size=batch_size, shuffle=False, drop_last=True, sampler=sampler) dataloader.dataset.classes = ['tshirt', 'pants', 'pullov', 'dress', 'coat','sandal', 'shirt', 'sneak', 'bag', 'ank-bt'] elif dataset =='mnist': if test: sampler = torch.utils.data.WeightedRandomSampler(weights=[1]*10000, num_samples=batch_size, replacement=True, generator=None) else: sampler = torch.utils.data.WeightedRandomSampler(weights=[1]*60000, num_samples=batch_size, replacement=True, generator=None) dataloader = torch.utils.data.DataLoader( datasets.MNIST(path, train=not(test), download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize([.5],[.5],[.5]) ])), batch_size=batch_size, shuffle=False, drop_last=True, sampler=sampler) dataloader.dataset.classes = ['zero', 'one', 'two', 'three', 'four', 'five','six', 'seven', 'eight', 'nine'] elif dataset =='sop': sampler = torch.utils.data.WeightedRandomSampler(weights=[1], num_samples=batch_size, replacement=True, generator=None) dataloader = torch.utils.data.DataLoader( datasets.ImageFolder(os.path.join(path,'Stanford_Online_Products'), transform=transforms.Compose([ transforms.Grayscale(), transforms.ToTensor(), transforms.Normalize([.5],[.5],[.5]) ])), batch_size=batch_size, shuffle=False, drop_last=True, sampler=sampler) dataloader.sampler.weights = torch.Tensor([1]*len(dataloader.dataset)) return dataloader

py

HC-MGAN

HC-MGAN-main/utils/soft_cluster.py

import os import numpy as np import math import matplotlib.pyplot as plt import torch import seaborn as sn import pandas as pd import numpy as np import math from sklearn import metrics import sklearn import scipy import scipy.optimize as opt import matplotlib.pyplot as plt import torchvision.transforms as transforms from torchvision.utils import save_image, make_grid from torch.utils.data import DataLoader from torchvision import datasets from torch.autograd import Variable from tqdm import tqdm_notebook import torch.nn as nn import torch.nn.functional as F import torch import shutil import time from utils.others import get_log_heading, get_bold_string, save_log_text, remove_bold_from_string, print_save_log CUDA = True if torch.cuda.is_available() else False Tensor = torch.cuda.FloatTensor if CUDA else torch.FloatTensor def view_global_tree_logs(dataloader_train, non_leaf_nodes, leaf_nodes, log_save_path, log_title = None, display_table=True): if log_title is None: log_title = '\nGLOBAL TREE LOGS AFTER LAST RAW SPLIT OR REFINEMENT' log_heading = get_bold_string(get_log_heading(log_title, spacing=2)) print_save_log('\n\n\n'+log_heading, log_save_path) no_classes = len(dataloader_train.dataset.classes) #----------------------- #LOGS PART 1: TREE NODES #----------------------- log_title = 'GLOBAL TREE LOGS 1/3: TREE NODES' log_heading = get_bold_string(get_log_heading(log_title)) print_save_log(log_heading, log_save_path) print_save_log('Non-leaf nodes:', log_save_path) print_save_log(str([node.name + ' (' + node.status + ')' for node in non_leaf_nodes]), log_save_path) print_save_log('\nLeaf nodes:', log_save_path) print_save_log(str([node.name + ' (' + node.status + ')' for node in leaf_nodes]), log_save_path) #------------------------------- #LOGS PART 2: CLUSTERING MATRIX #------------------------------- log_title = 'GLOBAL TREE LOGS 2/3: CLUSTERING MATRIX' log_heading = get_bold_string(get_log_heading(log_title)) print_save_log('\n\n'+log_heading, log_save_path) print_save_log("This table indicates the clustering matrix when it reaches N clusters, or N leaf nodes.", log_save_path) print_save_log("The final matrix occurs when N equals the number of classes.\n", log_save_path) print("(This table is saved at {})".format(log_save_path)) leaf_nodes_probs = [] for node in leaf_nodes: cluster_probs = node.cluster_probs leaf_nodes_probs.append(cluster_probs[-1].numpy()) leaf_nodes_probs = np.array(leaf_nodes_probs) cluster_counts_per_class = get_hard_cluster_per_class_parallel(dataloader_train, leaf_nodes_probs, no_of_classes=no_classes) cluster_matrix = np.array(cluster_counts_per_class) cluster_matrix_dict = {} classes_names_dict = {v:k for k,v in dataloader_train.dataset.class_to_idx.items()} classes_names = [classes_names_dict[t] for t in range(no_classes)] cluster_matrix_dict['Leaf Nodes Clusters'] = [node.name + ' (' + node.status + ')' for node in leaf_nodes] for i in range(len(cluster_matrix)): column_name = classes_names[i] + '({})'.format(np.sum(cluster_matrix[i]).round(2)) column_contents = cluster_matrix[i] cluster_matrix_dict[column_name] = column_contents if display_table: pd.set_option("max_colwidth", None) pd.set_option('max_columns', None) try: display(pd.DataFrame(cluster_matrix_dict)) print_save_log(str(pd.DataFrame(cluster_matrix_dict)), log_save_path, print_log=False) except: print_save_log(str(pd.DataFrame(cluster_matrix_dict)), log_save_path) #------------------------------- #LOGS PART 3: CLUSTERING METRICS #------------------------------- log_title = 'GLOBAL TREE LOGS 3/3: CLUSTERING METRICS' log_heading = get_bold_string(get_log_heading(log_title)) print_save_log('\n\n'+log_heading, log_save_path) #NMI nmi = get_parallel_clustering_nmi(cluster_counts_per_class) print_save_log("Normalized Mutual Information (NMI): {}".format(nmi), log_save_path) #max 1 class ACC classes_per_cluster, classes_counts_per_cluster = get_opt_assignment(1, cluster_counts_per_class) total_counts = np.sum([np.sum(classes_counts) for classes_counts in classes_counts_per_cluster]) total_data_examples= np.sum(cluster_counts_per_class) acc = total_counts/total_data_examples print_save_log('\nBest accuracy (ACC) with at most 1 (one) class per cluster: {}/{} = {}'.format(total_counts, total_data_examples, acc), log_save_path) opt_assign_string = 'Optimum assignment considered: \n' opt_assign_string += get_opt_assignment_str(classes_per_cluster, classes_counts_per_cluster, classes_names_dict, leaf_nodes) print_save_log(opt_assign_string, log_save_path) #ACC classes_per_cluster_best = [] classes_counts_per_cluster_best = [] total_counts_best = 0 for max_classes_per_cluster in range(1, no_classes+1): classes_per_cluster, classes_counts_per_cluster = get_opt_assignment(max_classes_per_cluster, cluster_counts_per_class) total_counts = np.sum([np.sum(classes_counts) for classes_counts in classes_counts_per_cluster]) if total_counts>total_counts_best: classes_per_cluster_best = classes_per_cluster classes_counts_per_cluster_best = classes_counts_per_cluster total_counts_best = total_counts acc = total_counts_best/total_data_examples print_save_log('\nBest accuracy (ACC) with multiple classes per cluster: {}/{} = {}'.format(total_counts_best, total_data_examples, acc), log_save_path) opt_assign_string = 'Optimum assignment considered: \n' opt_assign_string += get_opt_assignment_str(classes_per_cluster_best, classes_counts_per_cluster_best, classes_names_dict, leaf_nodes) print_save_log(opt_assign_string, log_save_path) print_save_log("\n(Note on the above ACC metrics: if the no. of classes is less then the no. clusters, " + "we can either consider multiple classes belonging to a single cluster or left certain classes unassigned for computing ACC. " + "The first ACC metric above considers at most 1 classes per cluster, and when the number of clusters and classes are equal, it provides the "+ "usual ACC metric used in horizontal clustering and also used in our paper as benchmark." + "The second ACC metric considers the best assignment possible with multiple classes allowed to be assigned to each cluster, " + "and its useful to track an upper bound for the final 1-to-1 ACC during the growth of the tree, before it reaches one cluster to each class.", log_save_path) def get_opt_assignment(max_classes_per_cluster, cluster_counts_per_class): """Gets optimum cluster assignment with hungarian algorithm, returning classes assignments and classes counts per cluster. For enabling multiple classes per cluster, the clustering matrix needs to have its cluster idx (columns) replicated n times, where n is the maximum number of classes allowed for each cluster. Args: max_classes_per cluster (int): maximum classes allowed for each cluster during the search for optimum assignment cluster_counts_per_class (int list): clustering matrix with axis 0 relating to classes and axis 1 to clusters """ #cluster matrix is repeated N times to allow max N classes per cluster mat = np.repeat(cluster_counts_per_class, max_classes_per_cluster, axis=1) #gets optimum assignment idxs and example counts lines, columns = scipy.optimize.linear_sum_assignment(mat, maximize=True) opt_assign_counts_per_cluster = np.array(mat)[lines, columns] #columns idxs refer to the N times repeated columns. #to get cluster idxs, we need the integer division of the repeated idxs by their repetition number columns_as_cluster_idx = columns//max_classes_per_cluster #for loop for getting class idxs and class counts for each cluster i classes_per_cluster = [] classes_counts_per_cluster = [] no_clusters = len(cluster_counts_per_class[0]) for i in range(no_clusters): classes_per_cluster.append(lines[columns_as_cluster_idx==i]) classes_counts_per_cluster.append(opt_assign_counts_per_cluster[columns_as_cluster_idx==i]) return classes_per_cluster, classes_counts_per_cluster def get_opt_assignment_str(classes_per_cluster, classes_counts_per_cluster, classes_names_dict, leaf_nodes): no_clusters = len(classes_per_cluster) opt_assign_string = '' for i in range(no_clusters): opt_assign_string += '[' opt_assign_string += ",".join(["'"+classes_names_dict[c]+"'({})".format(c_counts) for c,c_counts in zip(classes_per_cluster[i], classes_counts_per_cluster[i])]) opt_assign_string += ']' opt_assign_string += " --> '{}'; ".format(leaf_nodes[i].name) return opt_assign_string def get_hard_cluster_per_class_parallel(dataloader, split_cluster_probs, no_of_classes = 10, filter_classes=[]): max_mask = (split_cluster_probs.max(axis=0,keepdims=1) == split_cluster_probs) #print(max_mask[0]) no_of_clusters = len(split_cluster_probs) cluster_counts_per_class = [] cluster_probs_sum = split_cluster_probs[0] + split_cluster_probs[1] for i in range(no_of_classes): cluster_counts_ij = [] if i not in filter_classes: for j in range(no_of_clusters): #print(j) cluster_counts_ij.append( (((np.array(dataloader.dataset.targets)==i))*np.array(max_mask[j]) ).sum().item() ) cluster_counts_per_class.append(cluster_counts_ij) classes_names_dict = {v:k for k,v in dataloader.dataset.class_to_idx.items()} #print(np.array(cluster_counts_per_class)) return cluster_counts_per_class def get_parallel_clustering_nmi(cluster_counts_per_class): reference_labels = [] for i in range(len(cluster_counts_per_class)): reference_labels += [i]*np.array(cluster_counts_per_class[i]).sum() clustering_labels = [] for i in range(len(cluster_counts_per_class)): for j in range(len(cluster_counts_per_class[0])): clustering_labels += [j]*cluster_counts_per_class[i][j] #print(len(reference_labels)) #print(len(clustering_labels)) nmi = sklearn.metrics.cluster.normalized_mutual_info_score(reference_labels, clustering_labels) return nmi def show(img, rows): npimg = img.detach().numpy() plt.figure(figsize = (20, rows)) plt.imshow(np.transpose(npimg, (1,2,0)), interpolation='nearest') plt.axis('off') plt.show() def distribution_select(dist, shape): assert(dist in ['uniform', 'normal']) if dist=='uniform': return np.random.uniform(-1, 1, shape) elif dist=='normal': return np.random.normal(0, 1, shape) else: return None def get_local_cluster_table(clusters_per_class, classes_names_dict, node, table_name = 'Local binary clustering'): no_of_classes = len(clusters_per_class) classes_names = [classes_names_dict[c] for c in range(no_of_classes)] table_dict = {} left = node.child_left right = node.child_right table_dict[table_name] = ["Left cluster: " + left.name + "({})".format(left.status), "Right cluster: " + right.name + "({})".format(right.status)] for i in range(no_of_classes): column_name = classes_names[i] + '({})'.format(np.sum(clusters_per_class[i]).round(2)) classes_names_dict[i] column_contents = clusters_per_class[i] table_dict[column_name] = column_contents return pd.DataFrame(table_dict) def get_classification_table_variation(clusters_per_class_orig, clusters_per_class_new, classes_names_dict, node, data_prefix = '', table_name='Clustering result'): no_of_clusters = len(clusters_per_class_orig[0]) no_of_classes = len(clusters_per_class_orig) classes_names = [classes_names_dict[t] for t in range(no_of_classes)] table_dict = {} left = node.child_left right = node.child_right ordinal = lambda n: "%d%s" % (n,"tsnrhtdd"[(n//10%10!=1)*(n%10<4)*n%10::4]) table_dict[table_name] = ["Left cluster: " + left.name + "({})".format(left.status), "Right cluster: " + right.name + "({})".format(right.status)] clusters_per_class_diff = np.array(clusters_per_class_new) - np.array(clusters_per_class_orig) clusters_per_class_diff = clusters_per_class_diff.round(2) for i in range(no_of_classes): column_name = data_prefix + classes_names[i] + '({})'.format(np.sum(clusters_per_class_new[i]).round(2)) column_contents_new = clusters_per_class_new[i] column_contents_diff = clusters_per_class_diff[i] column_formatted = ['{} (+{})'.format(column_contents_new[j], column_contents_diff[j]) if column_contents_diff[j]>=0 else '{} ({})'.format(column_contents_new[j], column_contents_diff[j]) for j in range(len(clusters_per_class_new[i])) ] table_dict[column_name] = column_formatted return(pd.DataFrame(table_dict))

py

DKVMN

DKVMN-main/evaluation/run.py

""" Usage: run.py [options] Options: --length=<int> max length of question sequence [default: 50] --questions=<int> num of question [default: 100] --lr=<float> learning rate [default: 0.001] --bs=<int> batch size [default: 64] --seed=<int> random seed [default: 59] --epochs=<int> number of epochs [default: 30] --cuda=<int> use GPU id [default: 0] --final_fc_dim=<int> dimension of final dim [default: 10] --question_dim=<int> dimension of question dim[default: 50] --question_and_answer_dim=<int> dimension of question and answer dim [default: 100] --memory_size=<int> memory size [default: 20] --model=<string> model type [default: DKVMN] """ import os import random import logging import torch import torch.optim as optim import numpy as np from datetime import datetime from docopt import docopt from data.dataloader import getDataLoader from evaluation import eval def setup_seed(seed=0): random.seed(seed) np.random.seed(seed) torch.random.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def main(): args = docopt(__doc__) length = int(args['--length']) questions = int(args['--questions']) lr = float(args['--lr']) bs = int(args['--bs']) seed = int(args['--seed']) epochs = int(args['--epochs']) cuda = args['--cuda'] final_fc_dim = int(args['--final_fc_dim']) question_dim = int(args['--question_dim']) question_and_answer_dim = int(args['--question_and_answer_dim']) memory_size = int(args['--memory_size']) model_type = args['--model'] logger = logging.getLogger('main') logger.setLevel(level=logging.DEBUG) date = datetime.now() handler = logging.FileHandler( f'log/{date.year}_{date.month}_{date.day}_{model_type}_result.log') handler.setLevel(logging.INFO) formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.info('DKVMN') logger.info(list(args.items())) setup_seed(seed) if torch.cuda.is_available(): os.environ["CUDA_VISIBLE_DEVICES"] = cuda device = torch.device('cuda') else: device = torch.device('cpu') trainLoader, validationLoader, testLoader = getDataLoader(bs, questions, length) from model.model import MODEL model = MODEL(n_question=questions, batch_size=bs, q_embed_dim=question_dim, qa_embed_dim=question_and_answer_dim, memory_size=memory_size, final_fc_dim=final_fc_dim) model.init_params() model.init_embeddings() optimizer = optim.Adam(model.parameters(), lr=lr) best_auc = 0 for epoch in range(epochs): print('epoch: ' + str(epoch+1)) model, optimizer = eval.train_epoch(model, trainLoader, optimizer, device) logger.info(f'epoch {epoch+1}') auc = eval.test_epoch(model, validationLoader, device) if auc > best_auc: print('best checkpoint') torch.save({'state_dict': model.state_dict()}, 'checkpoint/'+model_type+'.pth.tar') best_auc = auc eval.test_epoch(model, testLoader, device, ckpt='checkpoint/'+model_type+'.pth.tar') if __name__ == '__main__': main()

py

DKVMN

DKVMN-main/evaluation/eval.py

import tqdm import torch import logging import os from sklearn import metrics logger = logging.getLogger('main.eval') def __load_model__(ckpt): ''' ckpt: Path of the checkpoint return: Checkpoint dict ''' if os.path.isfile(ckpt): checkpoint = torch.load(ckpt) print("Successfully loaded checkpoint '%s'" % ckpt) return checkpoint else: raise Exception("No checkpoint found at '%s'" % ckpt) def train_epoch(model, trainLoader, optimizer, device): model.to(device) for batch in tqdm.tqdm(trainLoader, desc='Training: ', mininterval=2): batch = batch.to(device) datas = torch.chunk(batch, 3, 2) optimizer.zero_grad() loss, prediction, ground_truth = model(datas[0].squeeze(2), datas[1].squeeze(2), datas[2]) loss.backward() optimizer.step() return model, optimizer def test_epoch(model, testLoader, device, ckpt=None): model.to(device) if ckpt is not None: checkpoint = __load_model__(ckpt) model.load_state_dict(checkpoint['state_dict']) ground_truth = torch.tensor([], device=device) prediction = torch.tensor([], device=device) for batch in tqdm.tqdm(testLoader, desc='Testing: ', mininterval=2): batch = batch.to(device) datas = torch.chunk(batch, 3, 2) loss, p, label = model(datas[0].squeeze(2), datas[1].squeeze(2), datas[2]) prediction = torch.cat([prediction, p]) ground_truth = torch.cat([ground_truth, label]) acc = metrics.accuracy_score(torch.round(ground_truth).detach().cpu().numpy(), torch.round(prediction).detach().cpu().numpy()) auc = metrics.roc_auc_score(ground_truth.detach().cpu().numpy(), prediction.detach().cpu().numpy()) logger.info('auc: ' + str(auc) + ' acc: ' + str(acc)) print('auc: ' + str(auc) + ' acc: ' + str(acc)) return auc

py

DKVMN

DKVMN-main/data/dataloader.py

import torch import torch.utils.data as Data from .readdata import DataReader #assist2015/assist2015_train.txt assist2015/assist2015_test.txt #assist2017/assist2017_train.txt assist2017/assist2017_test.txt #assist2009/builder_train.csv assist2009/builder_test.csv def getDataLoader(batch_size, num_of_questions, max_step): handle = DataReader('../dataset/assist2015/assist2015_train.txt', '../dataset/assist2015/assist2015_test.txt', max_step, num_of_questions) train, vali = handle.getTrainData() dtrain = torch.tensor(train.astype(int).tolist(), dtype=torch.long) dvali = torch.tensor(vali.astype(int).tolist(), dtype=torch.long) dtest = torch.tensor(handle.getTestData().astype(int).tolist(), dtype=torch.long) trainLoader = Data.DataLoader(dtrain, batch_size=batch_size, shuffle=True) valiLoader = Data.DataLoader(dvali, batch_size=batch_size, shuffle=True) testLoader = Data.DataLoader(dtest, batch_size=batch_size, shuffle=False) return trainLoader, valiLoader, testLoader

py

DKVMN

DKVMN-main/model/memory.py

import torch from torch import nn class DKVMNHeadGroup(nn.Module): def __init__(self, memory_size, memory_state_dim, is_write): super(DKVMNHeadGroup, self).__init__() """" Parameters memory_size: scalar memory_state_dim: scalar is_write: boolean """ self.memory_size = memory_size self.memory_state_dim = memory_state_dim self.is_write = is_write if self.is_write: self.erase = torch.nn.Linear(self.memory_state_dim, self.memory_state_dim, bias=True) self.add = torch.nn.Linear(self.memory_state_dim, self.memory_state_dim, bias=True) nn.init.kaiming_normal_(self.erase.weight) nn.init.kaiming_normal_(self.add.weight) nn.init.constant_(self.erase.bias, 0) nn.init.constant_(self.add.bias, 0) def addressing(self, control_input, memory): """ Parameters control_input: Shape (batch_size, control_state_dim) memory: Shape (memory_size, memory_state_dim) Returns correlation_weight: Shape (batch_size, memory_size) """ similarity_score = torch.matmul(control_input, torch.t(memory)) correlation_weight = torch.nn.functional.softmax(similarity_score, dim=1) # Shape: (batch_size, memory_size) return correlation_weight def read(self, memory, control_input=None, read_weight=None): """ Parameters control_input: Shape (batch_size, control_state_dim) memory: Shape (batch_size, memory_size, memory_state_dim) read_weight: Shape (batch_size, memory_size) Returns read_content: Shape (batch_size, memory_state_dim) """ if read_weight is None: read_weight = self.addressing(control_input=control_input, memory=memory) read_weight = read_weight.view(-1, 1) memory = memory.view(-1, self.memory_state_dim) rc = torch.mul(read_weight, memory) read_content = rc.view(-1, self.memory_size, self.memory_state_dim) read_content = torch.sum(read_content, dim=1) return read_content def write(self, control_input, memory, write_weight): """ Parameters control_input: Shape (batch_size, control_state_dim) write_weight: Shape (batch_size, memory_size) memory: Shape (batch_size, memory_size, memory_state_dim) Returns new_memory: Shape (batch_size, memory_size, memory_state_dim) """ assert self.is_write erase_signal = torch.sigmoid(self.erase(control_input)) add_signal = torch.tanh(self.add(control_input)) erase_reshape = erase_signal.view(-1, 1, self.memory_state_dim) add_reshape = add_signal.view(-1, 1, self.memory_state_dim) write_weight_reshape = write_weight.view(-1, self.memory_size, 1) erase_mult = torch.mul(erase_reshape, write_weight_reshape) add_mul = torch.mul(add_reshape, write_weight_reshape) new_memory = memory * (1 - erase_mult) + add_mul return new_memory class DKVMN(nn.Module): def __init__(self, memory_size, memory_key_state_dim, memory_value_state_dim, init_memory_key): super(DKVMN, self).__init__() """ :param memory_size: scalar :param memory_key_state_dim: scalar :param memory_value_state_dim: scalar :param init_memory_key: Shape (memory_size, memory_value_state_dim) :param init_memory_value: Shape (batch_size, memory_size, memory_value_state_dim) """ self.memory_size = memory_size self.memory_key_state_dim = memory_key_state_dim self.memory_value_state_dim = memory_value_state_dim self.key_head = DKVMNHeadGroup(memory_size=self.memory_size, memory_state_dim=self.memory_key_state_dim, is_write=False) self.value_head = DKVMNHeadGroup(memory_size=self.memory_size, memory_state_dim=self.memory_value_state_dim, is_write=True) self.memory_key = init_memory_key self.memory_value = None def init_value_memory(self, memory_value): self.memory_value = memory_value def attention(self, control_input): correlation_weight = self.key_head.addressing(control_input=control_input, memory=self.memory_key) return correlation_weight def read(self, read_weight): read_content = self.value_head.read(memory=self.memory_value, read_weight=read_weight) return read_content def write(self, write_weight, control_input): memory_value = self.value_head.write(control_input=control_input, memory=self.memory_value, write_weight=write_weight) self.memory_value = nn.Parameter(memory_value.data) return self.memory_value

py

DKVMN

DKVMN-main/model/model.py

import torch import torch.nn as nn from model.memory import DKVMN class MODEL(nn.Module): def __init__(self, n_question, batch_size, q_embed_dim, qa_embed_dim, memory_size, final_fc_dim): super(MODEL, self).__init__() self.n_question = n_question self.batch_size = batch_size self.q_embed_dim = q_embed_dim self.qa_embed_dim = qa_embed_dim self.memory_size = memory_size self.memory_key_state_dim = q_embed_dim self.memory_value_state_dim = qa_embed_dim self.final_fc_dim = final_fc_dim self.read_embed_linear = nn.Linear(self.memory_value_state_dim + self.memory_key_state_dim, self.final_fc_dim, bias=True) self.predict_linear = nn.Linear(self.final_fc_dim, 1, bias=True) self.init_memory_key = nn.Parameter(torch.randn(self.memory_size, self.memory_key_state_dim)) nn.init.kaiming_normal_(self.init_memory_key) self.init_memory_value = nn.Parameter(torch.randn(self.memory_size, self.memory_value_state_dim)) nn.init.kaiming_normal_(self.init_memory_value) self.mem = DKVMN(memory_size=self.memory_size, memory_key_state_dim=self.memory_key_state_dim, memory_value_state_dim=self.memory_value_state_dim, init_memory_key=self.init_memory_key) self.q_embed = nn.Embedding(self.n_question + 1, self.q_embed_dim, padding_idx=0) self.qa_embed = nn.Embedding(2 * self.n_question + 1, self.qa_embed_dim, padding_idx=0) def init_params(self): nn.init.kaiming_normal_(self.predict_linear.weight) nn.init.kaiming_normal_(self.read_embed_linear.weight) nn.init.constant_(self.read_embed_linear.bias, 0) nn.init.constant_(self.predict_linear.bias, 0) def init_embeddings(self): nn.init.kaiming_normal_(self.q_embed.weight) nn.init.kaiming_normal_(self.qa_embed.weight) def forward(self, q_data, qa_data, target): batch_size = q_data.shape[0] seqlen = q_data.shape[1] q_embed_data = self.q_embed(q_data) qa_embed_data = self.qa_embed(qa_data) memory_value = nn.Parameter(torch.cat([self.init_memory_value.unsqueeze(0) for _ in range(batch_size)], 0).data) self.mem.init_value_memory(memory_value) slice_q_embed_data = torch.chunk(q_embed_data, seqlen, 1) slice_qa_embed_data = torch.chunk(qa_embed_data, seqlen, 1) value_read_content_l = [] input_embed_l = [] for i in range(seqlen): # Attention q = slice_q_embed_data[i].squeeze(1) correlation_weight = self.mem.attention(q) # Read Process read_content = self.mem.read(correlation_weight) value_read_content_l.append(read_content) input_embed_l.append(q) # Write Process qa = slice_qa_embed_data[i].squeeze(1) self.mem.write(correlation_weight, qa) all_read_value_content = torch.cat([value_read_content_l[i].unsqueeze(1) for i in range(seqlen)], 1) input_embed_content = torch.cat([input_embed_l[i].unsqueeze(1) for i in range(seqlen)], 1) predict_input = torch.cat([all_read_value_content, input_embed_content], 2) read_content_embed = torch.tanh(self.read_embed_linear(predict_input.view(batch_size * seqlen, -1))) pred = self.predict_linear(read_content_embed) target_1d = target.view(-1, 1) # [batch_size * seq_len, 1] mask = target_1d.ge(1) # [batch_size * seq_len, 1] pred_1d = pred.view(-1, 1) # [batch_size * seq_len, 1] filtered_pred = torch.masked_select(pred_1d, mask) filtered_target = torch.masked_select(target_1d, mask) - 1 loss = torch.nn.functional.binary_cross_entropy_with_logits(filtered_pred, filtered_target.float()) return loss, torch.sigmoid(filtered_pred), filtered_target.float()

py

probabilistic-ensemble

probabilistic-ensemble-main/baseline_train.py

import numpy as np import matplotlib.pyplot as plt import tensorflow as tf import tensorflow_probability as tfp from ensemble_model import BaselineModel from noise_mnist_utils import normal_parse_params, rec_log_prob import pickle tfd = tfp.distributions config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config=config) class MnistBaseline(tf.keras.Model): def __init__(self, ensemble_num=3): super().__init__() self.ensemble_num = ensemble_num self.baseline_model = [BaselineModel() for _ in range(ensemble_num)] def ensemble_loss(self, obs, out_obs): total_loss = [] for idx in range(self.ensemble_num): single_loss = self.single_loss(tf.convert_to_tensor(obs), tf.convert_to_tensor(out_obs), idx) total_loss.append(single_loss * np.random.random()) return total_loss # return tf.convert_to_tensor(total_loss) def single_loss(self, obs, out_obs, i=0): """ 输出 variational lower bound, 训练目标是最大化该值. 输出维度 (batch,) """ rec_params = self.baseline_model[i](obs) rec_loss = -1.0 * rec_log_prob(rec_params=rec_params, s_next=out_obs) loss = tf.reduce_mean(rec_loss) return loss def build_dataset(train_images, train_labels, storage0=5, storage1=10): image_dict = {} # dict of image and label for idx in range(len(train_labels)): label = train_labels[idx] if label not in image_dict.keys(): image_dict[label] = [] else: image_dict[label].append(idx) # 构造数字0的样本 obs_idx0 = image_dict[0] # 抽取数字 0 的所有序号 np.random.shuffle(obs_idx0) train_x0, train_y0 = [], [] for idx in obs_idx0: for i in range(storage0): train_x0.append(idx) trans_to_idx = np.random.choice(image_dict[1]) train_y0.append(trans_to_idx) print("training data x0:", len(train_x0)) print("training data y0:", len(train_y0)) # 构造数字1的样本 obs_idx1 = image_dict[1] # 抽取数字 1 的所有序号 np.random.shuffle(obs_idx1) train_x1, train_y1 = [], [] for idx in obs_idx1: for i in range(storage1): train_x1.append(idx) trans_to_label = np.random.randint(low=2, high=10) trans_to_idx = np.random.choice(image_dict[trans_to_label]) train_y1.append(trans_to_idx) print("training data x1:", len(train_x1)) print("training data y1:", len(train_y1)) train_x0_img = train_images[train_x0] train_y0_img = train_images[train_y0] print("\ntraining data x0:", train_x0_img.shape) print("training data y0:", train_y0_img.shape) train_x1_img = train_images[train_x1] train_y1_img = train_images[train_y1] print("\ntraining data x1:", train_x1_img.shape) print("training data y1:", train_y1_img.shape) train_x_img = np.vstack([train_x0_img, train_x1_img]) train_y_img = np.vstack([train_y0_img, train_y1_img]) print("\ntraining data x:", train_x_img.shape) print("training data y:", train_y_img.shape) return train_x_img, train_y_img def mnist_data(build_train=True): # data (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() train_images = np.expand_dims((train_images / 255.).astype(np.float32), axis=-1) test_images = np.expand_dims((test_images / 255.).astype(np.float32), axis=-1) # Binarization train_images[train_images >= .5] = 1. train_images[train_images < .5] = 0. test_images[test_images >= .5] = 1. test_images[test_images < .5] = 0. # train if build_train: print("Generating training data:") train_x, train_y = build_dataset(train_images, train_labels, storage0=5, storage1=50) np.save('data/train_x.npy', train_x) np.save('data/train_y.npy', train_y) else: train_dataset = None print("Generating testing data:") test_x, test_y = build_dataset(test_images, test_labels, storage0=5, storage1=10) np.save('data/test_x.npy', test_x) np.save('data/test_y.npy', test_y) print("dataset done.") def load_mnist_data(): train_x = np.load("data/train_x.npy") train_y = np.load("data/train_y.npy") train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)).shuffle(500000) train_dataset = train_dataset.batch(512, drop_remainder=True) test_x = tf.convert_to_tensor(np.load("data/test_x.npy")) test_y = tf.convert_to_tensor(np.load("data/test_y.npy")) return train_dataset, test_x, test_y def train(): # model optimizer = tf.train.AdamOptimizer(learning_rate=1e-5) mnist_baseline = MnistBaseline() # data # mnist_data(build_train=True) # 先 run 这个来保存到本地 train_dataset, test_x, test_y = load_mnist_data() # start train Epochs = 500 test_loss = [] for epoch in range(Epochs): print("Epoch: ", epoch) for i, (batch_x, batch_y) in enumerate(train_dataset): with tf.GradientTape() as tape: # train loss_ensemble = mnist_baseline.ensemble_loss(batch_x, batch_y) loss = tf.reduce_mean(loss_ensemble) if i % 10 == 0: print(i, ", loss_ensemble:", [x.numpy() for x in loss_ensemble], ", loss:", loss.numpy(), flush=True) gradients = tape.gradient(loss, mnist_baseline.trainable_variables) # gradients, _ = tf.clip_by_global_norm(gradients, 1.) optimizer.apply_gradients(zip(gradients, mnist_baseline.trainable_variables)) # test t_loss = tf.reduce_mean(mnist_baseline.ensemble_loss(test_x, test_y)) test_loss.append(t_loss) print("Test Loss:", t_loss) # save mnist_baseline.save_weights("baseline_model/model_"+str(epoch)+".h5") np.save("baseline_model/test_loss.npy", np.array(test_loss)) if __name__ == '__main__': train()

py

probabilistic-ensemble

probabilistic-ensemble-main/baseline_generate.py

import numpy as np import matplotlib.pyplot as plt import tensorflow as tf import tensorflow_probability as tfp from ensemble_model import BaselineModel from noise_mnist_utils import normal_parse_params, rec_log_prob tfd = tfp.distributions config = tf.ConfigProto() config.gpu_options.allow_growth = True tf.enable_eager_execution(config=config) class MnistBaselineTest(tf.keras.Model): def __init__(self, ensemble_num=3): super().__init__() self.ensemble_num = ensemble_num self.baseline_model = [BaselineModel() for _ in range(ensemble_num)] def ensemble_loss(self, obs, out_obs): total_loss = [] for idx in range(self.ensemble_num): single_loss = self.single_loss(tf.convert_to_tensor(obs), tf.convert_to_tensor(out_obs), idx) total_loss.append(single_loss * np.random.random()) return total_loss def single_loss(self, obs, out_obs, i=0): """ 输出 variational lower bound, 训练目标是最大化该值. 输出维度 (batch,) """ rec_params = self.baseline_model[i](obs) rec_loss = -1.0 * rec_log_prob(rec_params=rec_params, s_next=out_obs) loss = tf.reduce_mean(rec_loss) return loss def generate_samples_params(self, obs): """ k 代表采样的个数. 从 prior network 输出分布中采样, 随后输入到 generative network 中采样 """ samples = [] for idx in range(self.ensemble_num): sample_params = self.baseline_model[idx](obs) # (batch,28,28,1) samples.append(sample_params[..., 0:1]) # take the mean return samples def build_test_dataset(): # data (_, _), (test_images, test_labels) = tf.keras.datasets.mnist.load_data() test_images = np.expand_dims((test_images / 255.).astype(np.float32), axis=-1) test_images[test_images >= .5] = 1. test_images[test_images < .5] = 0. image_dict = {} # dict of image and label for idx in range(len(test_labels)): label = test_labels[idx] if label not in image_dict.keys(): image_dict[label] = [] else: image_dict[label].append(idx) # 随机选择 idx0_random = np.random.choice(image_dict[0]) # 抽取数字 0 的所有序号 idx1_random = np.random.choice(image_dict[1]) # 抽取数字 1 的所有序号 test_x0 = test_images[idx0_random] # 转为图像 test_x1 = test_images[idx1_random] # 转为图像 return np.expand_dims(test_x0, axis=0), np.expand_dims(test_x1, axis=0) # shape=(1,28,28,1) def generate_0(model): test_x, _ = build_test_dataset() # 取到数字0 # sample samples = model.generate_samples_params(test_x) print([s.shape.as_list() for s in samples]) # plot plt.figure(figsize=(10, 10)) plt.subplot(1, len(samples) + 1, 1) plt.axis('off') plt.imshow(test_x[0, :, :, 0], cmap='gray') plt.title("input", fontsize=20) idx = 1 for sample in samples: sample = tf.nn.sigmoid(sample).numpy() # sample[sample >= 0.0] = 1. # sample[sample < 0.0] = 0. assert sample.shape == (1, 28, 28, 1) plt.subplot(1, len(samples)+1, idx+1) plt.axis('off') plt.imshow(sample[0, :, :, 0], cmap='gray') plt.title("model "+str(idx), fontsize=20) # plt.subplots_adjust(wspace=0., hspace=0.1) idx += 1 plt.savefig("baseline_model/Mnist-Ensemble-res0.pdf") # plt.show() plt.close() def generate_1(model): _, test_x = build_test_dataset() # 取到数字0 # sample samples = model.generate_samples_params(test_x) print([s.shape.as_list() for s in samples]) # plot plt.figure(figsize=(10, 10)) plt.subplot(1, len(samples) + 1, 1) plt.axis('off') plt.imshow(test_x[0, :, :, 0], cmap='gray') plt.title("input", fontsize=20) idx = 1 for sample in samples: sample = tf.nn.sigmoid(sample).numpy() # sample[sample >= 0.0] = 1. # sample[sample < 0.0] = 0. assert sample.shape == (1, 28, 28, 1) plt.subplot(1, len(samples)+1, idx+1) plt.axis('off') plt.imshow(sample[0, :, :, 0], cmap='gray') plt.title("model "+str(idx), fontsize=20) # plt.subplots_adjust(wspace=0., hspace=0.1) idx += 1 plt.savefig("baseline_model/Mnist-Ensemble-res1.pdf") # plt.show() plt.close() if __name__ == '__main__': # initialize model and load weights test_x0, test_x1 = build_test_dataset() ensemble_model = MnistBaselineTest() ensemble_model.ensemble_loss(tf.convert_to_tensor(test_x0), tf.convert_to_tensor(test_x0)) print("load weights...") ensemble_model.load_weights("baseline_model/model.h5") print("load done") # generate 0 print("Generate number 0") generate_0(ensemble_model) # generate 1 print("Generate number 1") generate_1(ensemble_model)

py

probabilistic-ensemble

probabilistic-ensemble-main/ensemble_model.py

import numpy as np import tensorflow as tf from noise_mnist_utils import normal_parse_params, rec_log_prob layers = tf.keras.layers tf.enable_eager_execution() class ResBlock(tf.keras.Model): """ Usual full pre-activation ResNet bottleneck block. """ def __init__(self, outer_dim, inner_dim): super(ResBlock, self).__init__() data_format = 'channels_last' self.net = tf.keras.Sequential([ layers.BatchNormalization(axis=-1), layers.LeakyReLU(), layers.Conv2D(inner_dim, (1, 1)), layers.BatchNormalization(axis=-1), layers.LeakyReLU(), layers.Conv2D(inner_dim, (3, 3), padding='same'), layers.BatchNormalization(axis=-1), layers.LeakyReLU(), layers.Conv2D(outer_dim, (1, 1))]) def call(self, x): return x + self.net(x) class MLPBlock(tf.keras.Model): def __init__(self, inner_dim): super(MLPBlock, self).__init__() self.net = tf.keras.Sequential([ layers.BatchNormalization(), layers.LeakyReLU(), layers.Conv2D(inner_dim, (1, 1))]) def call(self, x): return x + self.net(x) class EncoderNetwork(tf.keras.Model): def __init__(self): super(EncoderNetwork, self).__init__() self.net1 = tf.keras.Sequential([layers.Conv2D(8, 1), ResBlock(8, 8), ResBlock(8, 8), ResBlock(8, 8), ResBlock(8, 8)]) self.net2 = tf.keras.Sequential([layers.AveragePooling2D(2, 2), layers.Conv2D(16, 1), ResBlock(16, 8), ResBlock(16, 8), ResBlock(16, 8), ResBlock(16, 8)]) self.net3 = tf.keras.Sequential([layers.AveragePooling2D(2, 2), layers.Conv2D(32, 1), ResBlock(32, 16), ResBlock(32, 16), ResBlock(32, 16), ResBlock(32, 16)]) self.pad3 = tf.keras.layers.ZeroPadding2D(padding=((1, 0), (1, 0))) self.net4 = tf.keras.Sequential([layers.AveragePooling2D(2, 2), layers.Conv2D(64, 1), ResBlock(64, 32), ResBlock(64, 32), ResBlock(64, 32), ResBlock(64, 32)]) self.net5 = tf.keras.Sequential([layers.AveragePooling2D(2, 2), layers.Conv2D(128, 1), ResBlock(128, 64), ResBlock(128, 64), ResBlock(128, 64), ResBlock(128, 64)]) self.net6 = tf.keras.Sequential([layers.AveragePooling2D(2, 2), layers.Conv2D(128, 1), MLPBlock(128), MLPBlock(128), MLPBlock(128), MLPBlock(128)]) def call(self, x): # 当输入是 (None, 28, 28, 2), x = self.net1(x) # (b, 28, 28, 8) x = self.net2(x) # (b, 14, 14, 16) x = self.net3(x) # (b, 7, 7, 32) x = self.pad3(x) # (b, 8, 8, 32) x = self.net4(x) # (b, 4, 4, 64) x = self.net5(x) # (b, 2, 2, 128) x = self.net6(x) # (b, 1, 1, 128) return x class DecoderNetwork(tf.keras.Model): def __init__(self): super(DecoderNetwork, self).__init__() self.net1 = tf.keras.Sequential([layers.Conv2D(128, 1), MLPBlock(128), MLPBlock(128), MLPBlock(128), MLPBlock(128), layers.Conv2D(128, 1), layers.UpSampling2D((2, 2))]) self.net2 = tf.keras.Sequential([layers.Conv2D(128, 1), ResBlock(128, 64), ResBlock(128, 64), ResBlock(128, 64), ResBlock(128, 64), layers.Conv2D(64, 1), layers.UpSampling2D((2, 2))]) self.net3 = tf.keras.Sequential([layers.Conv2D(64, 1), ResBlock(64, 32), ResBlock(64, 32), ResBlock(64, 32), ResBlock(64, 32), layers.Conv2D(32, 1), layers.UpSampling2D((2, 2))]) self.net4 = tf.keras.Sequential([layers.Conv2D(32, 1), ResBlock(32, 16), ResBlock(32, 16), ResBlock(32, 16), ResBlock(32, 16), layers.Conv2D(16, 1), layers.UpSampling2D((2, 2))]) self.net5 = tf.keras.Sequential([layers.Conv2D(16, 1), ResBlock(16, 8), ResBlock(16, 8), ResBlock(16, 8), ResBlock(16, 8), layers.Conv2D(8, 1), layers.UpSampling2D((2, 2))]) self.net6 = tf.keras.Sequential([layers.Conv2D(8, 1), ResBlock(8, 8), ResBlock(8, 8), ResBlock(8, 8), ResBlock(8, 8), layers.Conv2D(4, 1)]) self.net7 = tf.keras.Sequential([layers.Conv2D(2, 1), ResBlock(2, 2), ResBlock(2, 2), ResBlock(2, 2), layers.Conv2D(2, 1)]) def call(self, x): # input=(b, 1, 1, 128) x = self.net1(x) # (b, 2, 2, 128) x = self.net2(x) # (b, 4, 4, 64) x = self.net3(x) # (b, 8, 8, 32) x = x[:, :-1, :-1, :] # (b, 7, 7, 32) x = self.net4(x) # (b, 14, 14, 16) x = self.net5(x) # (b, 28, 28, 8) x = self.net6(x) # (b, 28, 28, 4) x = self.net7(x) # (b, 28, 28, 2) return x class BaselineModel(tf.keras.Model): def __init__(self): super(BaselineModel, self).__init__() self.encoder_network = EncoderNetwork() self.decoder_network = DecoderNetwork() def call(self, x): en = self.encoder_network(x) de = self.decoder_network(en) return de if __name__ == '__main__': encoder_network = EncoderNetwork() x1 = tf.convert_to_tensor(np.random.random((2, 28, 28, 2)), tf.float32) y2 = encoder_network(x1) print("output of encoder network:", y2.shape) decoder_network = DecoderNetwork() x2 = tf.convert_to_tensor(np.random.random((2, 1, 1, 128)), tf.float32) y2 = decoder_network(x2) print("output of decoder network:", y2.shape) baseline_model = BaselineModel() x3 = tf.convert_to_tensor(np.random.random((2, 28, 28, 1)), tf.float32) y3 = baseline_model(x3) print("output of baseline networks:", y3.shape) print("Parameters:", np.sum([np.prod(v.shape.as_list()) for v in encoder_network.trainable_variables])) print("Parameters:", np.sum([np.prod(v.shape.as_list()) for v in decoder_network.trainable_variables])) print("Total Para:", np.sum([np.prod(v.shape.as_list()) for v in baseline_model.trainable_variables])) rec_params = baseline_model(x3) rec_loss = -1.0 * rec_log_prob(rec_params=rec_params, s_next=x3) print("rec_loss:", rec_loss.shape) loss = tf.reduce_mean(rec_loss) print("loss:", loss)

py

finer

finer-main/finer.py

import itertools import logging import os import time import re import datasets import numpy as np import tensorflow as tf import wandb from copy import deepcopy from tqdm import tqdm from gensim.models import KeyedVectors from seqeval.metrics import classification_report from seqeval.scheme import IOB2 from tensorflow.keras.preprocessing.sequence import pad_sequences from transformers import BertTokenizer, AutoTokenizer from wandb.keras import WandbCallback from configurations import Configuration from data import DATA_DIR, VECTORS_DIR from models import BiLSTM, Transformer, TransformerBiLSTM from models.callbacks import ReturnBestEarlyStopping, F1MetricCallback LOGGER = logging.getLogger(__name__) class DataLoader(tf.keras.utils.Sequence): def __init__(self, dataset, vectorize_fn, batch_size=8, max_length=128, shuffle=False): self.dataset = dataset self.vectorize_fn = vectorize_fn self.batch_size = batch_size if Configuration['general_parameters']['debug']: self.indices = np.arange(100) else: self.indices = np.arange(len(dataset)) self.max_length = max_length self.shuffle = shuffle if self.shuffle: np.random.shuffle(self.indices) def __len__(self): """Denotes the numbers of batches per epoch""" return int(np.ceil(len(self.indices) / self.batch_size)) def __getitem__(self, index): """Generate one batch of data""" # Generate indexes of the batch indices = self.indices[index * self.batch_size:(index + 1) * self.batch_size] # Find list of batch's sequences + targets samples = self.dataset[indices] x_batch, y_batch = self.vectorize_fn(samples=samples, max_length=self.max_length) return x_batch, y_batch def on_epoch_end(self): """Updates indexes after each epoch""" if self.shuffle: np.random.shuffle(self.indices) class FINER: def __init__(self): self.general_params = Configuration['general_parameters'] self.train_params = Configuration['train_parameters'] self.hyper_params = Configuration['hyper_parameters'] self.eval_params = Configuration['evaluation'] self.tag2idx, self.idx2tag = FINER.load_dataset_tags() self.n_classes = len(self.tag2idx) if Configuration['task']['mode'] == 'train': display_name = Configuration['task']['log_name'] if Configuration['task']['model'] == 'transformer': display_name = f"{display_name}_{self.train_params['model_name']}".replace('/', '-') elif Configuration['task']['model'] == 'bilstm': display_name = f"{display_name}_bilstm_{self.train_params['embeddings']}" wandb.init( entity=self.general_params['wandb_entity'], project=self.general_params['wandb_project'], id=Configuration['task']['log_name'], name=display_name ) shape_special_tokens_path = os.path.join(DATA_DIR, 'shape_special_tokens.txt') with open(shape_special_tokens_path) as fin: self.shape_special_tokens = [shape.strip() for shape in fin.readlines()] self.shape_special_tokens_set = set(self.shape_special_tokens) if Configuration['task']['model'] == 'bilstm': if 'subword' in self.train_params['embeddings']: self.train_params['token_type'] = 'subword' else: self.train_params['token_type'] = 'word' word_vector_path = os.path.join(VECTORS_DIR, self.train_params['embeddings']) if not os.path.exists(word_vector_path): import wget url = f"https://zenodo.org/record/6571000/files/{self.train_params['embeddings']}" wget.download(url=url, out=word_vector_path) if not os.path.exists(word_vector_path): raise Exception(f"Unable to download {self.train_params['embeddings']} embeddings") if word_vector_path.endswith('.vec') or word_vector_path.endswith('.txt'): word2vector = KeyedVectors.load_word2vec_format(word_vector_path, binary=False) else: word2vector = KeyedVectors.load_word2vec_format(word_vector_path, binary=True) if self.train_params['token_type'] == 'subword': import tempfile with tempfile.NamedTemporaryFile(mode='w') as tmp: vocab_tokens = ['[PAD]', '[CLS]', '[SEP]', '[MASK]'] + list(word2vector.index_to_key) tmp.write('\n'.join(vocab_tokens)) additional_special_tokens = [] if 'num' in self.train_params['embeddings']: additional_special_tokens.append('[NUM]') elif 'shape' in self.train_params['embeddings']: additional_special_tokens.append('[NUM]') additional_special_tokens.extend(self.shape_special_tokens) # TODO: Check AutoTokenizer self.tokenizer = BertTokenizer( vocab_file=tmp.name, use_fast=self.train_params['use_fast_tokenizer'] ) if additional_special_tokens: self.tokenizer.additional_special_tokens = additional_special_tokens if self.train_params['token_type'] == 'word': self.word2index = {'[PAD]': 0, '[UNK]': 1} self.word2index.update({word: i + 2 for i, word in enumerate(word2vector.index_to_key)}) self.word2vector_weights = np.concatenate( [ np.mean(word2vector.vectors, axis=0).reshape((1, word2vector.vectors.shape[-1])), word2vector.vectors ], axis=0 ) self.word2vector_weights = np.concatenate( [ np.zeros((1, self.word2vector_weights.shape[-1]), dtype=np.float32), self.word2vector_weights ], axis=0 ) if self.train_params['token_type'] == 'subword': self.word2index = {'[PAD]': 0} self.word2index.update({word: i + 1 for i, word in enumerate(word2vector.index_to_key)}) self.word2vector_weights = np.concatenate( [ np.zeros((1, word2vector.vectors.shape[-1]), dtype=np.float32), word2vector.vectors ], axis=0 ) self.index2word = {v: k for k, v in self.word2index.items()} elif Configuration['task']['model'] == 'transformer': additional_special_tokens = [] if self.train_params['replace_numeric_values']: additional_special_tokens.append('[NUM]') if self.train_params['replace_numeric_values'] == 'SHAPE': additional_special_tokens.extend(self.shape_special_tokens) self.tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path=self.train_params['model_name'], additional_special_tokens=additional_special_tokens, use_fast=self.train_params['use_fast_tokenizer'] ) @staticmethod def load_dataset_tags(): dataset = datasets.load_dataset('nlpaueb/finer-139', split='train', streaming=True) dataset_tags = dataset.features['ner_tags'].feature.names tag2idx = {tag: int(i) for i, tag in enumerate(dataset_tags)} idx2tag = {idx: tag for tag, idx in tag2idx.items()} return tag2idx, idx2tag def is_numeric_value(self, text): digits, non_digits = 0, 0 for char in str(text): if char.isdigit(): digits = digits + 1 else: non_digits += 1 return (digits + 1) > non_digits def vectorize(self, samples, max_length): if Configuration['task']['model'] == 'bilstm' and self.train_params['token_type'] == 'word': sample_tokens = [ [ token.lower() for token in sample ] for sample in samples['tokens'] ] if 'word.num' in self.train_params['embeddings']: sample_tokens = [ [ '[NUM]' if re.fullmatch(r'(\d+[\d,.]*)|([,.]\d+)', token) else token for token in sample ] for sample in sample_tokens ] elif 'word.shape' in self.train_params['embeddings']: for sample_idx, _ in enumerate(sample_tokens): for token_idx, _ in enumerate(sample_tokens[sample_idx]): if re.fullmatch(r'(\d+[\d,.]*)|([,.]\d+)', sample_tokens[sample_idx][token_idx]): shape = '[' + re.sub(r'\d', 'X', sample_tokens[sample_idx][token_idx]) + ']' if shape in self.shape_special_tokens_set: sample_tokens[sample_idx][token_idx] = shape else: sample_tokens[sample_idx][token_idx] = '[NUM]' word_indices = [ [ self.word2index[token] if token in self.word2index else self.word2index['[UNK]'] for token in sample ] for sample in sample_tokens ] word_indices = pad_sequences( sequences=word_indices, maxlen=max_length, padding='post', truncating='post' ) x = word_indices elif Configuration['task']['model'] == 'transformer' \ or (Configuration['task']['model'] == 'bilstm' and self.train_params['token_type'] == 'subword'): sample_tokens = samples['tokens'] sample_labels = samples['ner_tags'] batch_token_ids, batch_tags, batch_subword_pooling_mask = [], [], [] for sample_idx in range(len(sample_tokens)): sample_token_ids, sample_tags, subword_pooling_mask = [], [], [] sample_token_idx = 1 # idx 0 is reserved for [CLS] for token_idx in range(len(sample_tokens[sample_idx])): if (Configuration['task']['model'] == 'transformer' and self.train_params['model_name'] == 'nlpaueb/sec-bert-num') \ or (Configuration['task']['model'] == 'bilstm' and 'subword.num' in self.train_params['embeddings']): if re.fullmatch(r'(\d+[\d,.]*)|([,.]\d+)', sample_tokens[sample_idx][token_idx]): sample_tokens[sample_idx][token_idx] = '[NUM]' if (Configuration['task']['model'] == 'transformer' and self.train_params['model_name'] == 'nlpaueb/sec-bert-shape') \ or (Configuration['task']['model'] == 'bilstm' and 'subword.shape' in self.train_params['embeddings']): if re.fullmatch(r'(\d+[\d,.]*)|([,.]\d+)', sample_tokens[sample_idx][token_idx]): shape = '[' + re.sub(r'\d', 'X', sample_tokens[sample_idx][token_idx]) + ']' if shape in self.shape_special_tokens_set: sample_tokens[sample_idx][token_idx] = shape else: sample_tokens[sample_idx][token_idx] = '[NUM]' if self.train_params['replace_numeric_values']: if self.is_numeric_value(sample_tokens[sample_idx][token_idx]): if re.fullmatch(r'(\d+[\d,.]*)|([,.]\d+)', sample_tokens[sample_idx][token_idx]): if self.train_params['replace_numeric_values'] == 'NUM': sample_tokens[sample_idx][token_idx] = '[NUM]' elif self.train_params['replace_numeric_values'] == 'SHAPE': shape = '[' + re.sub(r'\d', 'X', sample_tokens[sample_idx][token_idx]) + ']' if shape in self.shape_special_tokens_set: sample_tokens[sample_idx][token_idx] = shape else: sample_tokens[sample_idx][token_idx] = '[NUM]' token = sample_tokens[sample_idx][token_idx] # Subword pooling (As in BERT or Acs et al.) if 'subword_pooling' in self.train_params: label_to_assign = self.idx2tag[sample_labels[sample_idx][token_idx]] if self.train_params['subword_pooling'] == 'all': # First token is B-, rest are I- if label_to_assign.startswith('B-'): remaining_labels = 'I' + label_to_assign[1:] else: remaining_labels = label_to_assign elif self.train_params['subword_pooling'] in ['first', 'last']: remaining_labels = 'O' else: raise Exception(f'Choose a valid subword pooling ["all", "first" and "last"] in the train parameters.') # Assign label to all (multiple) generated tokens, if any token_ids = self.tokenizer(token, add_special_tokens=False).input_ids sample_token_idx += len(token_ids) sample_token_ids.extend(token_ids) for i in range(len(token_ids)): if self.train_params['subword_pooling'] in ['first', 'all']: if i == 0: sample_tags.append(label_to_assign) subword_pooling_mask.append(1) else: if self.train_params['subword_pooling'] == 'first': subword_pooling_mask.append(0) sample_tags.append(remaining_labels) elif self.train_params['subword_pooling'] == 'last': if i == len(token_ids) - 1: sample_tags.append(label_to_assign) subword_pooling_mask.append(1) else: sample_tags.append(remaining_labels) subword_pooling_mask.append(0) if Configuration['task']['model'] == 'transformer': # if 'bert' in self.general_params['token_type']: CLS_ID = self.tokenizer.vocab['[CLS]'] SEP_ID = self.tokenizer.vocab['[SEP]'] PAD_ID = self.tokenizer.vocab['[PAD]'] sample_token_ids = [CLS_ID] + sample_token_ids + [SEP_ID] sample_tags = ['O'] + sample_tags + ['O'] subword_pooling_mask = [1] + subword_pooling_mask + [1] # Append to batch_token_ids & batch_tags batch_token_ids.append(sample_token_ids) batch_tags.append(sample_tags) batch_subword_pooling_mask.append(subword_pooling_mask) if Configuration['task']['model'] == 'bilstm' and self.train_params['token_type'] == 'subword': for sent_idx, _ in enumerate(batch_token_ids): for tok_idx, _ in enumerate(batch_token_ids[sent_idx]): token_subword = self.tokenizer.convert_ids_to_tokens( batch_token_ids[sent_idx][tok_idx], skip_special_tokens=True) batch_token_ids[sent_idx][tok_idx] = self.word2index[token_subword] \ if token_subword in self.word2index else self.word2index['[UNK]'] # Pad, truncate and verify # Returns an np.array object of shape ( len(batch_size) x max_length ) that contains padded/truncated gold labels batch_token_ids = pad_sequences( sequences=batch_token_ids, maxlen=max_length, padding='post', truncating='post' ) # Replace last column with SEP special token if it's not PAD if Configuration['task']['model'] == 'transformer': batch_token_ids[np.where(batch_token_ids[:, -1] != PAD_ID)[0], -1] = SEP_ID x = batch_token_ids else: x = None if Configuration['task']['model'] == 'bilstm' and self.train_params['token_type'] == 'word': y = pad_sequences( sequences=samples['ner_tags'], maxlen=max_length, padding='post', truncating='post' ) elif Configuration['task']['model'] == 'transformer' \ or (Configuration['task']['model'] == 'bilstm' and self.train_params['token_type'] == 'subword'): batch_tags = [[self.tag2idx[tag] for tag in sample_tags] for sample_tags in batch_tags] # Pad/Truncate the rest tags/labels y = pad_sequences( sequences=batch_tags, maxlen=max_length, padding='post', truncating='post' ) if Configuration['task']['model'] == 'transformer': y[np.where(x[:, -1] != PAD_ID)[0], -1] = 0 if self.train_params['subword_pooling'] in ['first', 'last']: batch_subword_pooling_mask = pad_sequences( sequences=batch_subword_pooling_mask, maxlen=max_length, padding='post', truncating='post' ) return [np.array(x), batch_subword_pooling_mask], y else: return np.array(x), y def build_model(self, train_params=None): if Configuration['task']['model'] == 'bilstm': model = BiLSTM( n_classes=self.n_classes, n_layers=train_params['n_layers'], n_units=train_params['n_units'], dropout_rate=train_params['dropout_rate'], crf=train_params['crf'], word2vectors_weights=self.word2vector_weights, ) elif Configuration['task']['model'] == 'transformer': model = Transformer( model_name=train_params['model_name'], n_classes=self.n_classes, dropout_rate=train_params['dropout_rate'], crf=train_params['crf'], tokenizer=self.tokenizer if self.train_params['replace_numeric_values'] else None, subword_pooling=self.train_params['subword_pooling'] ) elif Configuration['task']['model'] == 'transformer_bilstm': model = TransformerBiLSTM( model_name=train_params['model_name'], n_classes=self.n_classes, dropout_rate=train_params['dropout_rate'], crf=train_params['crf'], n_layers=train_params['n_layers'], n_units=train_params['n_units'], tokenizer=self.tokenizer if self.train_params['replace_numeric_values'] else None, ) else: raise Exception(f"The model type that you entered isn't a valid one.") return model def get_monitor(self): monitor_metric = self.general_params['loss_monitor'] if monitor_metric == 'val_loss': monitor_mode = 'min' elif monitor_metric in ['val_micro_f1', 'val_macro_f1']: monitor_mode = 'max' else: raise Exception(f'Unrecognized monitor: {self.general_params["loss_monitor"]}') return monitor_metric, monitor_mode def train(self): train_dataset = datasets.load_dataset(path='nlpaueb/finer-139', split='train') train_generator = DataLoader( dataset=train_dataset, vectorize_fn=self.vectorize, batch_size=self.general_params['batch_size'], max_length=self.train_params['max_length'], shuffle=True ) validation_dataset = datasets.load_dataset(path='nlpaueb/finer-139', split='validation') validation_generator = DataLoader( dataset=validation_dataset, vectorize_fn=self.vectorize, batch_size=self.general_params['batch_size'], max_length=self.train_params['max_length'], shuffle=False ) test_dataset = datasets.load_dataset(path='nlpaueb/finer-139', split='test') test_generator = DataLoader( dataset=test_dataset, vectorize_fn=self.vectorize, batch_size=self.general_params['batch_size'], max_length=self.train_params['max_length'], shuffle=False ) train_params = deepcopy(self.train_params) train_params.update(self.hyper_params) # Build model model = self.build_model(train_params=train_params) LOGGER.info('Model Summary') model.print_summary(print_fn=LOGGER.info) optimizer = tf.keras.optimizers.Adam(learning_rate=train_params['learning_rate'], clipvalue=5.0) if train_params['crf']: model.compile( optimizer=optimizer, loss=model.crf_layer.loss, run_eagerly=self.general_params['run_eagerly'] ) else: model.compile( optimizer=optimizer, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), run_eagerly=self.general_params['run_eagerly'] ) monitor, monitor_mode = self.get_monitor() # Init callbacks callbacks = [] f1_metric = F1MetricCallback( train_params=train_params, idx2tag=self.idx2tag, validation_generator=validation_generator, subword_pooling=self.train_params['subword_pooling'], calculate_train_metric=False ) callbacks.append(f1_metric) callbacks.append( ReturnBestEarlyStopping( monitor=monitor, mode=monitor_mode, patience=self.general_params['early_stopping_patience'], restore_best_weights=True, verbose=1 ) ) callbacks.append( tf.keras.callbacks.ReduceLROnPlateau( monitor=monitor, mode=monitor_mode, factor=0.5, cooldown=self.general_params['reduce_lr_cooldown'], patience=self.general_params['reduce_lr_patience'], verbose=1 ) ) if Configuration['task']['model'] == 'transformer': wandb.config.update( { 'model': 'transformer', 'model_name': self.train_params['model_name'], } ) elif Configuration['task']['model'] == 'bilstm': wandb.config.update( { 'model': 'bilstm', 'embedddings': self.train_params['embeddings'], } ) wandb.config.update( { 'max_length': self.train_params['max_length'], 'replace_numeric_values': self.train_params['replace_numeric_values'], 'subword_pooling': self.train_params['subword_pooling'], 'epochs': self.general_params['epochs'], 'batch_size': self.general_params['batch_size'], 'loss_monitor': self.general_params['loss_monitor'], 'early_stopping_patience': self.general_params['early_stopping_patience'], 'reduce_lr_patience': self.general_params['reduce_lr_patience'], 'reduce_lr_cooldown': self.general_params['reduce_lr_cooldown'] } ) wandb.config.update(self.hyper_params) callbacks.append( WandbCallback( monitor=monitor, mode=monitor_mode, ) ) # Train model start = time.time() history = model.fit( x=train_generator, validation_data=validation_generator, callbacks=callbacks, epochs=self.general_params['epochs'], workers=self.general_params['workers'], max_queue_size=self.general_params['max_queue_size'], use_multiprocessing=self.general_params['use_multiprocessing'] ) # Loss Report self.loss_report(history.history) # Save model weights_save_path = os.path.join(Configuration['experiment_path'], 'model', 'weights.h5') LOGGER.info(f'Saving model weights to {weights_save_path}') model.save_weights(filepath=weights_save_path) # Evaluate self.evaluate(model, validation_generator, split_type='validation') self.evaluate(model, test_generator, split_type='test') training_time = time.time() - start training_days = int(training_time / (24 * 60 * 60)) if training_days: LOGGER.info(f'Training time: {training_days} days {time.strftime("%H:%M:%S", time.gmtime(training_time))} sec\n') else: LOGGER.info(f'Training time: {time.strftime("%H:%M:%S", time.gmtime(training_time))} sec\n') def evaluate(self, model, generator, split_type): """ :param model: the trained TF model :param generator: the generator for the split type to evaluate on :param split_type: validation or test :return: """ LOGGER.info(f'\n{split_type.capitalize()} Evaluation\n{"-" * 30}\n') LOGGER.info('Calculating predictions...') y_true, y_pred = [], [] for x_batch, y_batch in tqdm(generator, ncols=100): if self.train_params['subword_pooling'] in ['first', 'last']: pooling_mask = x_batch[1] x_batch = x_batch[0] y_prob_temp = model.predict(x=[x_batch, pooling_mask]) else: pooling_mask = x_batch y_prob_temp = model.predict(x=x_batch) # Get lengths and cut results for padded tokens lengths = [len(np.where(x_i != 0)[0]) for x_i in x_batch] if model.crf: y_pred_temp = y_prob_temp.astype('int32') else: y_pred_temp = np.argmax(y_prob_temp, axis=-1) for y_true_i, y_pred_i, l_i, p_i in zip(y_batch, y_pred_temp, lengths, pooling_mask): if Configuration['task']['model'] == 'transformer': if self.train_params['subword_pooling'] in ['first', 'last']: y_true.append(np.take(y_true_i, np.where(p_i != 0)[0])[1:-1]) y_pred.append(np.take(y_pred_i, np.where(p_i != 0)[0])[1:-1]) else: y_true.append(y_true_i[1:l_i - 1]) y_pred.append(y_pred_i[1:l_i - 1]) elif Configuration['task']['model'] == 'bilstm': if self.train_params['subword_pooling'] in ['first', 'last']: y_true.append(np.take(y_true_i, np.where(p_i != 0)[0])) y_pred.append(np.take(y_pred_i, np.where(p_i != 0)[0])) else: y_true.append(y_true_i[:l_i]) y_pred.append(y_pred_i[:l_i]) # Indices to labels in one flattened list seq_y_pred_str = [] seq_y_true_str = [] for y_pred_row, y_true_row in zip(y_pred, y_true): # For each sequence seq_y_pred_str.append( [self.idx2tag[idx] for idx in y_pred_row.tolist()]) # Append list with sequence tokens seq_y_true_str.append( [self.idx2tag[idx] for idx in y_true_row.tolist()]) # Append list with sequence tokens flattened_seq_y_pred_str = list(itertools.chain.from_iterable(seq_y_pred_str)) flattened_seq_y_true_str = list(itertools.chain.from_iterable(seq_y_true_str)) assert len(flattened_seq_y_true_str) == len(flattened_seq_y_pred_str) # TODO: Check mode (strict, not strict) and scheme cr = classification_report( y_true=[flattened_seq_y_true_str], y_pred=[flattened_seq_y_pred_str], zero_division=0, mode=None, digits=3, scheme=IOB2 ) LOGGER.info(cr) def evaluate_pretrained_model(self): train_params = deepcopy(self.train_params) train_params.update(self.hyper_params) # Build model and load weights manually model = self.build_model(train_params=train_params) # Fake forward pass to get variables LOGGER.info('Model Summary') model.print_summary(print_fn=LOGGER.info) # Load weights by checkpoint model.load_weights(os.path.join(self.eval_params['pretrained_model_path'], 'weights.h5')) for split in self.eval_params['splits']: if split not in ['train', 'validation', 'test']: raise Exception(f'Invalid split selected ({split}). Valid options are "train", "validation", "test"') dataset = datasets.load_dataset(path='nlpaueb/finer-139', split=split) generator = DataLoader( dataset=dataset, vectorize_fn=self.vectorize, batch_size=self.general_params['batch_size'], max_length=self.train_params['max_length'], shuffle=False ) self.evaluate(model=model, generator=generator, split_type=split) def loss_report(self, history): """ Prints the loss report of the trained model :param history: The history dictionary that tensorflow returns upon completion of fit function """ best_epoch_by_loss = np.argmin(history['val_loss']) + 1 n_epochs = len(history['val_loss']) val_loss_per_epoch = '- ' + ' '.join('-' if history['val_loss'][i] < np.min(history['val_loss'][:i]) else '+' for i in range(1, len(history['val_loss']))) report = f'\nBest epoch by Val Loss: {best_epoch_by_loss}/{n_epochs}\n' report += f'Val Loss per epoch: {val_loss_per_epoch}\n\n' loss_dict = { 'loss': 'Loss', 'val_loss': 'Val Loss', 'val_micro_f1': 'Val Micro F1', 'val_macro_f1': 'Val Macro F1' } monitor_metric, monitor_mode = self.get_monitor() if monitor_metric != 'val_loss': argmin_max_fn = np.argmin if monitor_mode == 'min' else np.argmax min_max_fn = np.min if monitor_mode == 'min' else np.max best_epoch_by_monitor = argmin_max_fn(history[monitor_metric]) + 1 val_monitor_per_epoch = '- ' if monitor_mode == 'min' else '+ ' + ' '.join( '-' if history[monitor_metric][i] < min_max_fn(history[monitor_metric][:i]) else '+' for i in range(1, len(history[monitor_metric]))) monitor_metric_str = " ".join([s.capitalize() for s in monitor_metric.replace('val_', '').split("_")]) val_monitor_metric_str = " ".join([s.capitalize() for s in monitor_metric.split("_")]) report += f'Best epoch by {val_monitor_metric_str}: {best_epoch_by_monitor}/{n_epochs}\n' report += f'{val_monitor_metric_str} per epoch: {val_monitor_per_epoch}\n\n' # loss_dict[monitor_metric.replace('val_', '')] = monitor_metric_str # loss_dict[monitor_metric] = val_monitor_metric_str report += f"Loss & {monitor_metric_str} Report\n{'-' * 100}\n" else: report += f"Loss Report\n{'-' * 100}\n" report += f"Loss Report\n{'-' * 120}\n" report += 'Epoch | ' report += ' | '.join([f"{loss_nick:<17}" for loss_name, loss_nick in loss_dict.items() if loss_name in history]) report += ' | Learning Rate' + '\n' for n_epoch in range(len(history['loss'])): report += f'Epoch #{n_epoch + 1:3.0f} | ' for loss_name in loss_dict.keys(): if loss_name in history: report += f'{history[loss_name][n_epoch]:1.6f}' + ' ' * 10 report += '| ' report += f'{history["lr"][n_epoch]:.3e}' + '\n' LOGGER.info(report)

py

finer

finer-main/models/callbacks.py

import logging import numpy as np import itertools from tqdm import tqdm from seqeval.metrics.sequence_labeling import precision_recall_fscore_support from tensorflow.keras.callbacks import Callback, EarlyStopping from configurations import Configuration LOGGER = logging.getLogger(__name__) class ReturnBestEarlyStopping(EarlyStopping): def __init__(self, **kwargs): super(ReturnBestEarlyStopping, self).__init__(**kwargs) def on_train_end(self, logs=None): if self.stopped_epoch > 0: if self.verbose > 0: print(f'\nEpoch {self.stopped_epoch + 1}: early stopping') elif self.restore_best_weights: if self.verbose > 0: print('Restoring model weights from the end of the best epoch.') self.model.set_weights(self.best_weights) class F1MetricCallback(Callback): def __init__( self, train_params, idx2tag, train_generator=None, validation_generator=None, subword_pooling='all', calculate_train_metric=False ): super(F1MetricCallback, self).__init__() if validation_generator is None: raise Exception(f'F1MetricCallback: Please provide a validation generator') if calculate_train_metric and train_generator is None: raise Exception(f'F1MetricCallback: Please provide a train generator') self.train_params = train_params self.idx2tag = idx2tag self.train_generator = train_generator self.validation_generator = validation_generator self.subword_pooling = subword_pooling self.calculate_train_metric = calculate_train_metric def on_epoch_end(self, epoch, logs=None): if logs is None: logs = {} if self.calculate_train_metric: train_micro_precision, train_micro_recall, train_micro_f1, \ train_macro_precision, train_macro_recall, train_macro_f1, train_support = \ self.evaluate(generator=self.train_generator) logs[f'micro_precision'] = train_micro_precision logs[f'micro_recall'] = train_micro_recall logs[f'micro_f1'] = train_micro_f1 logs[f'macro_precision'] = train_macro_precision logs[f'macro_recall'] = train_macro_recall logs[f'macro_f1'] = train_macro_f1 val_micro_precision, val_micro_recall, val_micro_f1, \ val_macro_precision, val_macro_recall, val_macro_f1, val_support = \ self.evaluate(generator=self.validation_generator) logs[f'val_micro_precision'] = val_micro_precision logs[f'val_micro_recall'] = val_micro_recall logs[f'val_micro_f1'] = val_micro_f1 logs[f'val_macro_precision'] = val_macro_precision logs[f'val_macro_recall'] = val_macro_recall logs[f'val_macro_f1'] = val_macro_f1 def evaluate(self, generator): y_true, y_pred = [], [] for x_batch, y_batch in tqdm(generator, ncols=100): if self.subword_pooling in ['first', 'last']: pooling_mask = x_batch[1] x_batch = x_batch[0] y_prob_temp = self.model.predict(x=[x_batch, pooling_mask]) else: pooling_mask = x_batch y_prob_temp = self.model.predict(x=x_batch) # Get lengths and cut results for padded tokens lengths = [len(np.where(x_i != 0)[0]) for x_i in x_batch] if self.model.crf: y_pred_temp = y_prob_temp.astype('int32') else: y_pred_temp = np.argmax(y_prob_temp, axis=-1) for y_true_i, y_pred_i, l_i, p_i in zip(y_batch, y_pred_temp, lengths, pooling_mask): if Configuration['task']['model'] == 'transformer': if self.subword_pooling in ['first', 'last']: y_true.append(np.take(y_true_i, np.where(p_i != 0)[0])[1:-1]) y_pred.append(np.take(y_pred_i, np.where(p_i != 0)[0])[1:-1]) else: y_true.append(y_true_i[1:l_i - 1]) y_pred.append(y_pred_i[1:l_i - 1]) elif Configuration['task']['model'] == 'bilstm': if self.subword_pooling in ['first', 'last']: y_true.append(np.take(y_true_i, np.where(p_i != 0)[0])) y_pred.append(np.take(y_pred_i, np.where(p_i != 0)[0])) else: y_true.append(y_true_i[:l_i]) y_pred.append(y_pred_i[:l_i]) # Indices to labels list of lists seq_y_pred_str = [] seq_y_true_str = [] for y_pred_row, y_true_row in zip(y_pred, y_true): seq_y_pred_str.append([self.idx2tag[idx] for idx in y_pred_row.tolist()]) seq_y_true_str.append([self.idx2tag[idx] for idx in y_true_row.tolist()]) flattened_seq_y_pred_str = list(itertools.chain.from_iterable(seq_y_pred_str)) flattened_seq_y_true_str = list(itertools.chain.from_iterable(seq_y_true_str)) assert len(flattened_seq_y_true_str) == len(flattened_seq_y_pred_str) precision_micro, recall_micro, f1_micro, support = precision_recall_fscore_support( y_true=[flattened_seq_y_true_str], y_pred=[flattened_seq_y_pred_str], average='micro', warn_for=('f-score',), beta=1, zero_division=0 ) precision_macro, recall_macro, f1_macro, support = precision_recall_fscore_support( y_true=[flattened_seq_y_true_str], y_pred=[flattened_seq_y_pred_str], average='macro', warn_for=('f-score',), beta=1, zero_division=0 ) return precision_micro, recall_micro, f1_micro, precision_macro, recall_macro, f1_macro, support

py

finer

finer-main/models/transformer_bilstm.py

import tensorflow as tf import numpy as np from transformers import AutoTokenizer, TFAutoModel from tf2crf import CRF class TransformerBiLSTM(tf.keras.Model): def __init__( self, model_name, n_classes, dropout_rate=0.1, crf=False, n_layers=1, n_units=128, tokenizer=None, subword_pooling='all' ): super().__init__() self.n_classes = n_classes self.dropout_rate = dropout_rate self.crf = crf self.n_layers = n_layers self.n_units = n_units self.subword_pooling = subword_pooling self.encoder = TFAutoModel.from_pretrained( pretrained_model_name_or_path=model_name ) if tokenizer: self.encoder.resize_token_embeddings( new_num_tokens=len(tokenizer.vocab)) self.bilstm_layers = [ tf.keras.layers.Bidirectional( tf.keras.layers.LSTM( units=n_units, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name=f'BiLSTM_{i + 1}' ) ) for i in range(n_layers) ] if self.crf: self.classifier = tf.keras.layers.Dense( units=n_classes, activation=None ) # Pass logits to a custom CRF Layer self.crf_layer = CRF(output_dim=n_classes, mask=True) else: self.classifier = tf.keras.layers.Dense( units=n_classes, activation='softmax' ) def call(self, inputs, training=None, mask=None): if self.subword_pooling in ['first', 'last']: pooling_mask = inputs[1] inputs = inputs[0] encodings = self.bert_encoder(inputs)[0] encodings = tf.keras.layers.SpatialDropout1D( rate=self.dropout_rate )(encodings, training=training) for i, bilstm_layer in enumerate(self.bilstm_layers): encodings = bilstm_layer(encodings) encodings = tf.keras.layers.SpatialDropout1D( rate=self.dropout_rate )(encodings, training=training) outputs = self.classifier(encodings) if self.crf: outputs = self.crf_layer(outputs, mask=tf.not_equal(inputs, 0)) if self.subword_pooling in ['first', 'last']: outputs = tf.cast(tf.expand_dims(pooling_mask, axis=-1), dtype=tf.float32) * outputs return outputs def print_summary(self, line_length=None, positions=None, print_fn=None): # Fake forward pass to build graph batch_size, sequence_length = 1, 32 inputs = np.ones((batch_size, sequence_length), dtype=np.int32) if self.subword_pooling in ['first', 'last']: pooling_mask = np.ones((batch_size, sequence_length), dtype=np.int32) inputs = [inputs, pooling_mask] self.predict(inputs) self.summary(line_length=line_length, positions=positions, print_fn=print_fn) if __name__ == '__main__': from tensorflow.keras.preprocessing.sequence import pad_sequences # Init random seeds np.random.seed(1) tf.random.set_seed(1) model_name = 'nlpaueb/sec-bert-base' # Build test model model = TransformerBiLSTM( model_name=model_name, n_classes=10, dropout_rate=0.2, crf=False, n_layers=1, n_units=128, subword_pooling='all' ) # inputs = pad_sequences(np.random.randint(0, 30000, (5, 32)), maxlen=64, padding='post', truncating='post') inputs = [ 'This is the first sentence', 'This is the second sentence', 'This is the third sentence', 'This is the fourth sentence', 'This is the last sentence, this is a longer sentence'] tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path=model_name, use_fast=True ) inputs = tokenizer.batch_encode_plus( batch_text_or_text_pairs=inputs, add_special_tokens=False, max_length=64, padding='max_length', return_tensors='tf' ).input_ids outputs = pad_sequences(np.random.randint(0, 10, (5, 32)), maxlen=64, padding='post', truncating='post') optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5, clipvalue=5.0) if model.crf: model.compile( optimizer=optimizer, loss=model.crf_layer.loss, run_eagerly=True ) else: model.compile( optimizer=optimizer, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), run_eagerly=True ) print(model.print_summary(line_length=150)) model.fit(x=inputs, y=outputs, batch_size=2) model.predict(inputs, batch_size=1) predictions = model.predict(inputs, batch_size=2) print(predictions)

py

finer

finer-main/models/bilstm.py

import tensorflow as tf import numpy as np from tf2crf import CRF class BiLSTM(tf.keras.Model): def __init__( self, n_classes, n_layers=1, n_units=128, dropout_rate=0.1, crf=False, word2vectors_weights=None, subword_pooling='all' ): super().__init__() self.n_classes = n_classes self.n_layers = n_layers self.n_units = n_units self.dropout_rate = dropout_rate self.crf = crf self.subword_pooling = subword_pooling self.embeddings = tf.keras.layers.Embedding( input_dim=len(word2vectors_weights), output_dim=word2vectors_weights.shape[-1], weights=[word2vectors_weights], trainable=False, mask_zero=True ) self.bilstm_layers = [] for i in range(n_layers): self.bilstm_layers.append( tf.keras.layers.Bidirectional( tf.keras.layers.LSTM( units=n_units, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, name=f'BiLSTM_{i+1}' ) ) ) if self.crf: self.classifier = tf.keras.layers.Dense( units=n_classes, activation=None ) # Pass logits to a custom CRF Layer self.crf_layer = CRF(output_dim=n_classes, mask=True) else: self.classifier = tf.keras.layers.Dense( units=n_classes, activation='softmax' ) def call(self, inputs, training=None, mask=None): if self.subword_pooling in ['first', 'last']: pooling_mask = inputs[1] inputs = inputs[0] inner_inputs = self.embeddings(inputs) for i, bilstm_layer in enumerate(self.bilstm_layers): encodings = bilstm_layer(inner_inputs) if i != 0: inner_inputs = tf.keras.layers.add([inner_inputs, encodings]) else: inner_inputs = encodings inner_inputs = tf.keras.layers.SpatialDropout1D( rate=self.dropout_rate )(inner_inputs, training=training) outputs = self.classifier(inner_inputs) if self.crf: outputs = self.crf_layer(outputs, mask=tf.not_equal(inputs, 0)) if self.subword_pooling in ['first', 'last']: outputs = tf.cast(tf.expand_dims(pooling_mask, axis=-1), dtype=tf.float32) * outputs return outputs def print_summary(self, line_length=None, positions=None, print_fn=None): # Fake forward pass to build graph batch_size, sequence_length = 1, 32 inputs = np.ones((batch_size, sequence_length), dtype=np.int32) if self.subword_pooling in ['first', 'last']: pooling_mask = np.ones((batch_size, sequence_length), dtype=np.int32) inputs = [inputs, pooling_mask] self.predict(inputs) self.summary(line_length=line_length, positions=positions, print_fn=print_fn) if __name__ == '__main__': from tensorflow.keras.preprocessing.sequence import pad_sequences # Init random seeds np.random.seed(1) tf.random.set_seed(1) # Build test model word2vectors_weights = np.random.random((30000, 200)) model = BiLSTM( n_classes=10, n_layers=2, n_units=128, dropout_rate=0.1, crf=True, word2vectors_weights=word2vectors_weights, subword_pooling='all' ) inputs = pad_sequences(np.random.randint(0, 30000, (5, 32)), maxlen=64, padding='post', truncating='post') outputs = pad_sequences(np.random.randint(0, 10, (5, 32)), maxlen=64, padding='post', truncating='post') if model.crf: model.compile(optimizer='adam', loss=model.crf_layer.loss, run_eagerly=True) else: model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', run_eagerly=True) model.print_summary(line_length=150) model.fit(x=inputs, y=outputs, batch_size=2) predictions = model.predict(inputs, batch_size=2) print(predictions)

py

finer

finer-main/models/transformer.py

import tensorflow as tf import numpy as np from transformers import AutoTokenizer, TFAutoModel from tf2crf import CRF class Transformer(tf.keras.Model): def __init__( self, model_name, n_classes, dropout_rate=0.1, crf=False, tokenizer=None, subword_pooling='all' ): super().__init__() self.model_name = model_name self.n_classes = n_classes self.dropout_rate = dropout_rate self.crf = crf self.subword_pooling = subword_pooling self.encoder = TFAutoModel.from_pretrained( pretrained_model_name_or_path=model_name ) if tokenizer: self.encoder.resize_token_embeddings( new_num_tokens=len(tokenizer.vocab)) if self.crf: self.classifier = tf.keras.layers.Dense( units=n_classes, activation=None ) # Pass logits to a custom CRF Layer self.crf_layer = CRF(output_dim=n_classes, mask=True) else: self.classifier = tf.keras.layers.Dense( units=n_classes, activation='softmax' ) def call(self, inputs, training=None, mask=None): if self.subword_pooling in ['first', 'last']: pooling_mask = inputs[1] inputs = inputs[0] encodings = self.encoder(inputs)[0] encodings = tf.keras.layers.SpatialDropout1D( rate=self.dropout_rate )(encodings, training=training) outputs = self.classifier(encodings) if self.crf: outputs = self.crf_layer(outputs, mask=tf.not_equal(inputs, 0)) if self.subword_pooling in ['first', 'last']: outputs = tf.cast(tf.expand_dims(pooling_mask, axis=-1), dtype=tf.float32) * outputs return outputs def print_summary(self, line_length=None, positions=None, print_fn=None): # Fake forward pass to build graph batch_size, sequence_length = 1, 32 inputs = np.ones((batch_size, sequence_length), dtype=np.int32) if self.subword_pooling in ['first', 'last']: pooling_mask = np.ones((batch_size, sequence_length), dtype=np.int32) inputs = [inputs, pooling_mask] self.predict(inputs) self.summary(line_length=line_length, positions=positions, print_fn=print_fn) if __name__ == '__main__': from tensorflow.keras.preprocessing.sequence import pad_sequences # Init random seeds np.random.seed(1) tf.random.set_seed(1) model_name = 'nlpaueb/sec-bert-base' # Build test model model = Transformer( model_name=model_name, n_classes=10, dropout_rate=0.2, crf=True, tokenizer=None, subword_pooling='all' ) # inputs = pad_sequences(np.random.randint(0, 30000, (5, 32)), maxlen=64, padding='post', truncating='post') inputs = [ 'This is the first sentence', 'This is the second sentence', 'This is the third sentence', 'This is the fourth sentence', 'This is the last sentence, this is a longer sentence'] tokenizer = AutoTokenizer.from_pretrained( pretrained_model_name_or_path=model_name, use_fast=True ) inputs = tokenizer.batch_encode_plus( batch_text_or_text_pairs=inputs, add_special_tokens=False, max_length=64, padding='max_length', return_tensors='tf' ).input_ids outputs = pad_sequences(np.random.randint(0, 10, (5, 32)), maxlen=64, padding='post', truncating='post') optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5, clipvalue=5.0) if model.crf: model.compile( optimizer=optimizer, loss=model.crf_layer.loss, run_eagerly=True ) else: model.compile( optimizer=optimizer, loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), run_eagerly=True ) print(model.print_summary(line_length=150)) model.fit(x=inputs, y=outputs, batch_size=2) model.predict(inputs, batch_size=1) predictions = model.predict(inputs, batch_size=2) print(predictions)

py

UDAStrongBaseline

UDAStrongBaseline-master/sbs_traindbscan_unc.py

from __future__ import print_function, absolute_import import argparse import os.path as osp import random import numpy as np import sys from sklearn.cluster import DBSCAN # from sklearn.preprocessing import normalize import torch from torch import nn from torch.backends import cudnn from torch.utils.data import DataLoader import torch.nn.functional as F # from torch.nn import init # # from UDAsbs.utils.rerank import compute_jaccard_dist from UDAsbs import datasets, sinkhornknopp as sk from UDAsbs import models from UDAsbs.trainers import DbscanBaseTrainer_unc_ema from UDAsbs.evaluators import Evaluator, extract_features from UDAsbs.utils.data import IterLoader from UDAsbs.utils.data import transforms as T from UDAsbs.utils.data.sampler import RandomMultipleGallerySampler from UDAsbs.utils.data.preprocessor import Preprocessor from UDAsbs.utils.logging import Logger from UDAsbs.utils.serialization import load_checkpoint, save_checkpoint#, copy_state_dict from UDAsbs.memorybank.NCEAverage import onlinememory from UDAsbs.utils.faiss_rerank import compute_jaccard_distance # import ipdb start_epoch = best_mAP = 0 def get_data(name, data_dir, l=1): root = osp.join(data_dir) dataset = datasets.create(name, root, l) label_dict = {} for i, item_l in enumerate(dataset.train): # dataset.train[i]=(item_l[0],0,item_l[2]) if item_l[1] in label_dict: label_dict[item_l[1]].append(i) else: label_dict[item_l[1]] = [i] return dataset, label_dict def get_train_loader(dataset, height, width, choice_c, batch_size, workers, num_instances, iters, trainset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) train_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), T.ToTensor(), normalizer, T.RandomErasing(probability=0.5, mean=[0.596, 0.558, 0.497]) ]) train_set = trainset #dataset.train if trainset is None else trainset rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomMultipleGallerySampler(train_set, num_instances, choice_c) else: sampler = None train_loader = IterLoader( DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer, mutual=True), batch_size=batch_size, num_workers=workers, sampler=sampler, shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters) # train_loader = IterLoader( # DataLoader(UnsupervisedCamStylePreprocessor(train_set, root=dataset.images_dir, transform=train_transformer, # num_cam=dataset.num_cam,camstyle_dir=dataset.camstyle_dir, mutual=True), # batch_size=batch_size, num_workers=0, sampler=sampler,#workers # shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters) return train_loader def get_test_loader(dataset, height, width, batch_size, workers, testset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer ]) if (testset is None): testset = list(set(dataset.query) | set(dataset.gallery)) test_loader = DataLoader( Preprocessor(testset, root=dataset.images_dir, transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return test_loader from UDAsbs.models.dsbn import convert_dsbn from torch.nn import Parameter def copy_state_dict(state_dict, model, strip=None): tgt_state = model.state_dict() copied_names = set() for name, param in state_dict.items(): name = name.replace('module.', '') if strip is not None and name.startswith(strip): name = name[len(strip):] if name not in tgt_state: continue if isinstance(param, Parameter): param = param.data if param.size() != tgt_state[name].size(): print('mismatch:', name, param.size(), tgt_state[name].size()) continue tgt_state[name].copy_(param) copied_names.add(name) missing = set(tgt_state.keys()) - copied_names if len(missing) > 0: print("missing keys in state_dict:", missing) return model def create_model(args, ncs, wopre=False): model_1 = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) model_1_ema = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) if not wopre: initial_weights = load_checkpoint(args.init_1) copy_state_dict(initial_weights['state_dict'], model_1) copy_state_dict(initial_weights['state_dict'], model_1_ema) print('load pretrain model:{}'.format(args.init_1)) # adopt domain-specific BN convert_dsbn(model_1) convert_dsbn(model_1_ema) model_1.cuda() model_1_ema.cuda() model_1 = nn.DataParallel(model_1) model_1_ema = nn.DataParallel(model_1_ema) for i, cl in enumerate(ncs): exec('model_1_ema.module.classifier{}_{}.weight.data.copy_(model_1.module.classifier{}_{}.weight.data)'.format(i,cl,i,cl)) return model_1, None, model_1_ema, None def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True main_worker(args) class Optimizer: def __init__(self, target_label, m, dis_gt, t_loader,N, hc=3, ncl=None, n_epochs=200, weight_decay=1e-5, ckpt_dir='/',fc_len=3500): self.num_epochs = n_epochs self.momentum = 0.9 self.weight_decay = weight_decay self.checkpoint_dir = ckpt_dir self.N=N self.resume = True self.checkpoint_dir = None self.writer = None # model stuff self.hc = len(ncl)#10 self.K = ncl#3000 self.K_c =[fc_len for _ in range(len(ncl))] self.model = m self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.L = [torch.LongTensor(target_label[i]).to(self.dev) for i in range(len(self.K))] self.nmodel_gpus = 4#len() self.pseudo_loader = t_loader#torch.utils.data.DataLoader(t_loader,batch_size=256) # can also be DataLoader with less aug. self.train_loader = t_loader self.lamb = 25#args.lamb # the parameter lambda in the SK algorithm self.cpu=True self.dis_gt=dis_gt dtype_='f64' if dtype_ == 'f32': self.dtype = torch.float32 if not self.cpu else np.float32 else: self.dtype = torch.float64 if not self.cpu else np.float64 self.outs = self.K # activations of previous to last layer to be saved if using multiple heads. self.presize = 2048#4096 # def optimize_labels(self): if self.cpu: sk.cpu_sk(self) else: sk.gpu_sk(self) # save Label-assignments: optional # torch.save(self.L, os.path.join(self.checkpoint_dir, 'L', str(niter) + '_L.gz')) # free memory data = 0 self.PS = 0 return self.L import collections def func(x, a, b, c): return a * np.exp(-b * x) + c def write_sta_im(train_loader): label2num=collections.defaultdict(int) save_label=[] for x in train_loader: label2num[x[1]]+=1 save_label.append(x[1]) labels=sorted(label2num.items(),key=lambda item:item[1])[::-1] num = [j for i, j in labels] distribution = np.array(num)/len(train_loader) return num,save_label def print_cluster_acc(label_dict,target_label_tmp): num_correct = 0 for pid in label_dict: pid_index = np.asarray(label_dict[pid]) pred_label = np.argmax(np.bincount(target_label_tmp[pid_index])) num_correct += (target_label_tmp[pid_index] == pred_label).astype(np.float32).sum() cluster_accuracy = num_correct / len(target_label_tmp) print(f'cluster accucary: {cluster_accuracy:.3f}') class uncer(object): def __init__(self): self.sm = torch.nn.Softmax(dim=1) self.log_sm = torch.nn.LogSoftmax(dim=1) # self.cross_batch=CrossBatchMemory() self.kl_distance = nn.KLDivLoss(reduction='none') def kl_cal(self,pred1,pred1_ema): variance = torch.sum(self.kl_distance(self.log_sm(pred1), self.sm(pred1_ema.detach())), dim=1) exp_variance = torch.exp(-variance) return exp_variance def main_worker(args): global start_epoch, best_mAP cudnn.benchmark = True sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) print("==========\nArgs:{}\n==========".format(args)) # Create data loaders iters = args.iters if (args.iters > 0) else None ncs = [int(x) for x in args.ncs.split(',')] # ncs_dbscan=ncs.copy() dataset_target, label_dict = get_data(args.dataset_target, args.data_dir, len(ncs)) test_loader_target = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers) tar_cluster_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=dataset_target.train) dataset_source, _ = get_data(args.dataset_source, args.data_dir, len(ncs)) sour_cluster_loader = get_test_loader(dataset_source, args.height, args.width, args.batch_size, args.workers, testset=dataset_source.train) train_loader_source = get_train_loader(dataset_source, args.height, args.width, 0, args.batch_size, args.workers, args.num_instances, args.iters, dataset_source.train) source_classes = dataset_source.num_train_pids distribution,_ = write_sta_im(dataset_source.train) fc_len = 3500 model_1, _, model_1_ema, _ = create_model(args, [fc_len for _ in range(len(ncs))]) # print(model_1) epoch = 0 target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=100) target_features = F.normalize(torch.stack(list(target_features_dict.values())), dim=1) # Calculate distance print('==> Create pseudo labels for unlabeled target domain') rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) del target_features if (epoch == 0): # DBSCAN cluster eps = 0.6 # 0.6 print('Clustering criterion: eps: {:.3f}'.format(eps)) cluster = DBSCAN(eps=eps, min_samples=4, metric='precomputed', n_jobs=-1) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) # num_ids = len(set(pseudo_labels)) - (1 if -1 in pseudo_labels else 0) plabel=[] new_dataset=[] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1: continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) + 1] print('new class are {}, length of new dataset is {}'.format(ncs, len(new_dataset))) model_1.module.classifier0_3500 = nn.Linear(2048, ncs[0]+source_classes, bias=False).cuda() model_1_ema.module.classifier0_3500 = nn.Linear(2048, ncs[0]+source_classes, bias=False).cuda() model_1.module.classifier3_0_3500 = nn.Linear(1024, ncs[0]+source_classes, bias=False).cuda() model_1_ema.module.classifier3_0_3500 = nn.Linear(1024, ncs[0]+source_classes, bias=False).cuda() print(model_1.module.classifier0_3500) # if epoch !=0: # model_1.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # model_1_ema.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # Initialize source-domain class centroids print("==> Initialize source-domain class centroids in the hybrid memory") source_features, _ = extract_features(model_1, sour_cluster_loader, print_freq=50) sour_fea_dict = collections.defaultdict(list) print("==> Ending source-domain class centroids in the hybrid memory") for f, pid, _ in sorted(dataset_source.train): sour_fea_dict[pid].append(source_features[f].unsqueeze(0)) source_centers = [torch.cat(sour_fea_dict[pid], 0).mean(0) for pid in sorted(sour_fea_dict.keys())] source_centers = torch.stack(source_centers, 0) source_centers = F.normalize(source_centers, dim=1) del sour_fea_dict, source_features, sour_cluster_loader # Evaluator evaluator_1 = Evaluator(model_1) evaluator_1_ema = Evaluator(model_1_ema) clusters = [args.num_clusters] * args.epochs# TODO: dropout clusters k_memory=8192 contrast = onlinememory(2048, len(new_dataset),sour_numclass=source_classes,K=k_memory+source_classes, index2label=target_label, choice_c=args.choice_c, T=0.07, use_softmax=True).cuda() contrast.index_memory = torch.cat((torch.arange(source_classes), -1*torch.ones(k_memory).long()), dim=0).cuda() contrast.memory = torch.cat((source_centers, torch.rand(k_memory, 2048)), dim=0).cuda() tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=new_dataset) o = Optimizer(target_label, dis_gt=distribution, m=model_1, ncl=ncs, t_loader=tar_selflabel_loader, N=len(new_dataset), fc_len=fc_len) uncertainty=collections.defaultdict(list) print("Training begining~~~~~~!!!!!!!!!") for epoch in range(len(clusters)): iters_ = 300 if epoch % 1== 0 else iters if epoch % 6 == 0 and epoch !=0: target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=50) target_features = torch.stack(list(target_features_dict.values())) target_features = F.normalize(target_features, dim=1) print('==> Create pseudo labels for unlabeled target domain with') rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) plabel = [] new_dataset = [] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1:continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) + 1] tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=new_dataset) o = Optimizer(target_label, dis_gt=distribution, m=model_1, ncl=ncs, t_loader=tar_selflabel_loader, N=len(new_dataset),fc_len=fc_len) contrast.index_memory = torch.cat((torch.arange(source_classes), -1 * torch.ones(k_memory).long()), dim=0).cuda() model_1.module.classifier0_3500 = nn.Linear(2048, ncs[0] + source_classes, bias=False).cuda() model_1_ema.module.classifier0_3500 = nn.Linear(2048, ncs[0] + source_classes, bias=False).cuda() model_1.module.classifier3_0_3500 = nn.Linear(1024, ncs[0] + source_classes, bias=False).cuda() model_1_ema.module.classifier3_0_3500 = nn.Linear(1024, ncs[0] + source_classes, bias=False).cuda() print(model_1.module.classifier0_3500) # if epoch !=0: # model_1.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # model_1_ema.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) target_label_o = o.L target_label = [list(np.asarray(target_label_o[0].data.cpu())+source_classes)] contrast.index2label = [[i for i in range(source_classes)] + target_label[0]] # change pseudo labels for i in range(len(new_dataset)): new_dataset[i] = list(new_dataset[i]) for j in range(len(ncs)): new_dataset[i][j+1] = int(target_label[j][i]) new_dataset[i] = tuple(new_dataset[i]) cc=args.choice_c#(args.choice_c+1)%len(ncs) train_loader_target = get_train_loader(dataset_target, args.height, args.width, cc, args.batch_size, args.workers, args.num_instances, iters_, new_dataset) # Optimizer params = [] flag = 1.0 # if 20<epoch<=40 or 60<epoch<=80 or 120<epoch: # flag=0.1 # else: # flag=1.0 for key, value in model_1.named_parameters(): if not value.requires_grad: print(key) continue params += [{"params": [value], "lr": args.lr*flag, "weight_decay": args.weight_decay}] optimizer = torch.optim.Adam(params) # Trainer trainer = DbscanBaseTrainer_unc_ema(model_1, model_1_ema, contrast, None,None, num_cluster=ncs, c_name=ncs,alpha=args.alpha, fc_len=fc_len, source_classes=source_classes, uncer_mode=args.uncer_mode) train_loader_target.new_epoch() train_loader_source.new_epoch() trainer.train(epoch, train_loader_target, train_loader_source, optimizer, args.choice_c, lambda_tri=args.lambda_tri, lambda_ct=args.lambda_ct, lambda_reg=args.lambda_reg, print_freq=args.print_freq, train_iters=iters_,uncertainty_d=uncertainty) def save_model(model_ema, is_best, best_mAP, mid): save_checkpoint({ 'state_dict': model_ema.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join(args.logs_dir, 'model' + str(mid) + '_checkpoint.pth.tar')) if epoch==20: args.eval_step=2 elif epoch==40: args.eval_step=1 if ((epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1)): mAP_1 = 0#evaluator_1.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, # cmc_flag=False) mAP_2 = evaluator_1_ema.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=False) is_best = (mAP_1 > best_mAP) or (mAP_2 > best_mAP) best_mAP = max(mAP_1, mAP_2, best_mAP) save_model(model_1, (is_best), best_mAP, 1) save_model(model_1_ema, (is_best and (mAP_1 <= mAP_2)), best_mAP, 2) print('\n * Finished epoch {:3d} model no.1 mAP: {:5.1%} model no.2 mAP: {:5.1%} best: {:5.1%}{}\n'. format(epoch, mAP_1, mAP_2, best_mAP, ' *' if is_best else '')) if __name__ == '__main__': parser = argparse.ArgumentParser(description="MMT Training") # data parser.add_argument('-st', '--dataset-source', type=str, default='market1501', choices=datasets.names()) parser.add_argument('-tt', '--dataset-target', type=str, default='dukemtmc', choices=datasets.names()) parser.add_argument('-b', '--batch-size', type=int, default=64) parser.add_argument('-j', '--workers', type=int, default=8) parser.add_argument('--choice_c', type=int, default=0) parser.add_argument('--num-clusters', type=int, default=700) parser.add_argument('--ncs', type=str, default='60') parser.add_argument('--k1', type=int, default=30, help="hyperparameter for jaccard distance") parser.add_argument('--k2', type=int, default=6, help="hyperparameter for jaccard distance") parser.add_argument('--height', type=int, default=256, help="input height") parser.add_argument('--width', type=int, default=128, help="input width") parser.add_argument('--num-instances', type=int, default=4, help="each minibatch consist of " "(batch_size // num_instances) identities, and " "each identity has num_instances instances, " "default: 0 (NOT USE)") # model parser.add_argument('-a', '--arch', type=str, default='resnet50_multi', choices=models.names()) parser.add_argument('--features', type=int, default=0) parser.add_argument('--dropout', type=float, default=0) # optimizer parser.add_argument('--lr', type=float, default=0.00035, help="learning rate of new parameters, for pretrained " "parameters it is 10 times smaller than this") parser.add_argument('--momentum', type=float, default=0.9) parser.add_argument('--alpha', type=float, default=0.999) parser.add_argument('--moving-avg-momentum', type=float, default=0) parser.add_argument('--weight-decay', type=float, default=5e-4) parser.add_argument('--soft-ce-weight', type=float, default=0.5) parser.add_argument('--soft-tri-weight', type=float, default=0.8) parser.add_argument('--epochs', type=int, default=400) parser.add_argument('--iters', type=int, default=300) parser.add_argument('--lambda-value', type=float, default=0) # training configs parser.add_argument('--rr-gpu', action='store_true', help="use GPU for accelerating clustering") # parser.add_argument('--init-1', type=str, default='logs/personxTOpersonxval/resnet_ibn50a-pretrain-1_gem_RA//model_best.pth.tar', metavar='PATH') parser.add_argument('--init-1', type=str, default='logs/market1501TOdukemtmc/resnet50-pretrain-1005/model_best.pth.tar', metavar='PATH') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--print-freq', type=int, default=100) parser.add_argument('--eval-step', type=int, default=5) parser.add_argument('--n-jobs', type=int, default=8) # path working_dir = osp.dirname(osp.abspath(__file__)) parser.add_argument('--data-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'data')) parser.add_argument('--logs-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'logs/d2m_baseline/tmp')) parser.add_argument('--lambda-tri', type=float, default=1.0) parser.add_argument('--lambda-reg', type=float, default=1.0) parser.add_argument('--lambda-ct', type=float, default=0.05) parser.add_argument('--uncer-mode', type=float, default=0)#0 mean 1 max 2 min print("======mmt_train_dbscan_self-labeling=======") main()

py

UDAStrongBaseline

UDAStrongBaseline-master/sbs_traindbscan.py

from __future__ import print_function, absolute_import import argparse import os.path as osp import random import numpy as np import sys from sklearn.cluster import DBSCAN # from sklearn.preprocessing import normalize import torch from torch import nn from torch.backends import cudnn from torch.utils.data import DataLoader import torch.nn.functional as F # from torch.nn import init # # from UDAsbs.utils.rerank import compute_jaccard_dist from UDAsbs import datasets, sinkhornknopp as sk from UDAsbs import models from UDAsbs.trainers import DbscanBaseTrainer from UDAsbs.evaluators import Evaluator, extract_features from UDAsbs.utils.data import IterLoader from UDAsbs.utils.data import transforms as T from UDAsbs.utils.data.sampler import RandomMultipleGallerySampler from UDAsbs.utils.data.preprocessor import Preprocessor from UDAsbs.utils.logging import Logger from UDAsbs.utils.serialization import load_checkpoint, save_checkpoint#, copy_state_dict from UDAsbs.memorybank.NCEAverage import onlinememory from UDAsbs.utils.faiss_rerank import compute_jaccard_distance # import ipdb start_epoch = best_mAP = 0 def get_data(name, data_dir, l=1): root = osp.join(data_dir) dataset = datasets.create(name, root, l) label_dict = {} for i, item_l in enumerate(dataset.train): # dataset.train[i]=(item_l[0],0,item_l[2]) if item_l[1] in label_dict: label_dict[item_l[1]].append(i) else: label_dict[item_l[1]] = [i] return dataset, label_dict def get_train_loader(dataset, height, width, choice_c, batch_size, workers, num_instances, iters, trainset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) train_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), T.ToTensor(), normalizer, T.RandomErasing(probability=0.5, mean=[0.596, 0.558, 0.497]) ]) train_set = trainset #dataset.train if trainset is None else trainset rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomMultipleGallerySampler(train_set, num_instances, choice_c) else: sampler = None train_loader = IterLoader( DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer, mutual=True), batch_size=batch_size, num_workers=workers, sampler=sampler, shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters) return train_loader def get_test_loader(dataset, height, width, batch_size, workers, testset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer ]) if (testset is None): testset = list(set(dataset.query) | set(dataset.gallery)) test_loader = DataLoader( Preprocessor(testset, root=dataset.images_dir, transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return test_loader from UDAsbs.models.dsbn import convert_dsbn from torch.nn import Parameter def copy_state_dict(state_dict, model, strip=None): tgt_state = model.state_dict() copied_names = set() for name, param in state_dict.items(): name = name.replace('module.', '') if strip is not None and name.startswith(strip): name = name[len(strip):] if name not in tgt_state: continue if isinstance(param, Parameter): param = param.data if param.size() != tgt_state[name].size(): print('mismatch:', name, param.size(), tgt_state[name].size()) continue tgt_state[name].copy_(param) copied_names.add(name) missing = set(tgt_state.keys()) - copied_names if len(missing) > 0: print("missing keys in state_dict:", missing) return model def create_model(args, ncs, wopre=False): model_1 = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) model_1_ema = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) if not wopre: initial_weights = load_checkpoint(args.init_1) copy_state_dict(initial_weights['state_dict'], model_1) copy_state_dict(initial_weights['state_dict'], model_1_ema) print('load pretrain model:{}'.format(args.init_1)) # adopt domain-specific BN convert_dsbn(model_1) convert_dsbn(model_1_ema) model_1.cuda() model_1_ema.cuda() model_1 = nn.DataParallel(model_1) model_1_ema = nn.DataParallel(model_1_ema) for i, cl in enumerate(ncs): exec('model_1_ema.module.classifier{}_{}.weight.data.copy_(model_1.module.classifier{}_{}.weight.data)'.format(i,cl,i,cl)) return model_1, None, model_1_ema, None def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True main_worker(args) class Optimizer: def __init__(self, target_label, m, dis_gt, t_loader,N, hc=3, ncl=None, n_epochs=200, weight_decay=1e-5, ckpt_dir='/',fc_len=3500): self.num_epochs = n_epochs self.momentum = 0.9 self.weight_decay = weight_decay self.checkpoint_dir = ckpt_dir self.N=N self.resume = True self.checkpoint_dir = None self.writer = None # model stuff self.hc = len(ncl)#10 self.K = ncl#3000 self.K_c =[fc_len for _ in range(len(ncl))] self.model = m self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.L = [torch.LongTensor(target_label[i]).to(self.dev) for i in range(len(self.K))] self.nmodel_gpus = 4#len() self.pseudo_loader = t_loader#torch.utils.data.DataLoader(t_loader,batch_size=256) # can also be DataLoader with less aug. self.train_loader = t_loader self.lamb = 25#args.lamb # the parameter lambda in the SK algorithm self.cpu=True self.dis_gt=dis_gt dtype_='f64' if dtype_ == 'f32': self.dtype = torch.float32 if not self.cpu else np.float32 else: self.dtype = torch.float64 if not self.cpu else np.float64 self.outs = self.K # activations of previous to last layer to be saved if using multiple heads. self.presize = 2048#4096 # def optimize_labels(self): if self.cpu: sk.cpu_sk(self) else: sk.gpu_sk(self) # save Label-assignments: optional # torch.save(self.L, os.path.join(self.checkpoint_dir, 'L', str(niter) + '_L.gz')) # free memory data = 0 self.PS = 0 return self.L import collections def func(x, a, b, c): return a * np.exp(-b * x) + c def write_sta_im(train_loader): label2num=collections.defaultdict(int) save_label=[] for x in train_loader: label2num[x[1]]+=1 save_label.append(x[1]) labels=sorted(label2num.items(),key=lambda item:item[1])[::-1] num = [j for i, j in labels] distribution = np.array(num)/len(train_loader) return num,save_label def print_cluster_acc(label_dict,target_label_tmp): num_correct = 0 for pid in label_dict: pid_index = np.asarray(label_dict[pid]) pred_label = np.argmax(np.bincount(target_label_tmp[pid_index])) num_correct += (target_label_tmp[pid_index] == pred_label).astype(np.float32).sum() cluster_accuracy = num_correct / len(target_label_tmp) print(f'cluster accucary: {cluster_accuracy:.3f}') class uncer(object): def __init__(self): self.sm = torch.nn.Softmax(dim=1) self.log_sm = torch.nn.LogSoftmax(dim=1) # self.cross_batch=CrossBatchMemory() self.kl_distance = nn.KLDivLoss(reduction='none') def kl_cal(self,pred1,pred1_ema): variance = torch.sum(self.kl_distance(self.log_sm(pred1), self.sm(pred1_ema.detach())), dim=1) exp_variance = torch.exp(-variance) return exp_variance def main_worker(args): global start_epoch, best_mAP cudnn.benchmark = True sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) print("==========\nArgs:{}\n==========".format(args)) # Create data loaders iters = args.iters if (args.iters > 0) else None ncs = [int(x) for x in args.ncs.split(',')] # ncs_dbscan=ncs.copy() dataset_target, label_dict = get_data(args.dataset_target, args.data_dir, len(ncs)) test_loader_target = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers) tar_cluster_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=dataset_target.train) dataset_source, _ = get_data(args.dataset_source, args.data_dir, len(ncs)) sour_cluster_loader = get_test_loader(dataset_source, args.height, args.width, args.batch_size, args.workers, testset=dataset_source.train) train_loader_source = get_train_loader(dataset_source, args.height, args.width, 0, args.batch_size, args.workers, args.num_instances, args.iters, dataset_source.train) source_classes = dataset_source.num_train_pids distribution,_ = write_sta_im(dataset_source.train) fc_len = 3500 model_1, _, model_1_ema, _ = create_model(args, [fc_len for _ in range(len(ncs))]) # print(model_1) epoch = 0 target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=100) target_features = F.normalize(torch.stack(list(target_features_dict.values())), dim=1) # Calculate distance print('==> Create pseudo labels for unlabeled target domain') rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) del target_features if (epoch == 0): # DBSCAN cluster eps = 0.6 # 0.6 print('Clustering criterion: eps: {:.3f}'.format(eps)) cluster = DBSCAN(eps=eps, min_samples=4, metric='precomputed', n_jobs=-1) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) # num_ids = len(set(pseudo_labels)) - (1 if -1 in pseudo_labels else 0) plabel=[] new_dataset=[] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1: continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) + 1] print('new class are {}, length of new dataset is {}'.format(ncs, len(new_dataset))) model_1.module.classifier0_3500 = nn.Linear(2048, ncs[0]+source_classes, bias=False).cuda() model_1_ema.module.classifier0_3500 = nn.Linear(2048, ncs[0]+source_classes, bias=False).cuda() model_1.module.classifier3_0_3500 = nn.Linear(1024, ncs[0]+source_classes, bias=False).cuda() model_1_ema.module.classifier3_0_3500 = nn.Linear(1024, ncs[0]+source_classes, bias=False).cuda() print(model_1.module.classifier0_3500) # if epoch !=0: # model_1.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # model_1_ema.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # Initialize source-domain class centroids print("==> Initialize source-domain class centroids in the hybrid memory") source_features, _ = extract_features(model_1, sour_cluster_loader, print_freq=50) sour_fea_dict = collections.defaultdict(list) print("==> Ending source-domain class centroids in the hybrid memory") for f, pid, _ in sorted(dataset_source.train): sour_fea_dict[pid].append(source_features[f].unsqueeze(0)) source_centers = [torch.cat(sour_fea_dict[pid], 0).mean(0) for pid in sorted(sour_fea_dict.keys())] source_centers = torch.stack(source_centers, 0) source_centers = F.normalize(source_centers, dim=1) del sour_fea_dict, source_features, sour_cluster_loader # Evaluator evaluator_1 = Evaluator(model_1) evaluator_1_ema = Evaluator(model_1_ema) clusters = [args.num_clusters] * args.epochs# TODO: dropout clusters k_memory=8192 contrast = onlinememory(2048, len(new_dataset),sour_numclass=source_classes,K=k_memory+source_classes, index2label=target_label, choice_c=args.choice_c, T=0.07, use_softmax=True).cuda() contrast.index_memory = torch.cat((torch.arange(source_classes), -1*torch.ones(k_memory).long()), dim=0).cuda() contrast.memory = torch.cat((source_centers, torch.rand(k_memory, 2048)), dim=0).cuda() tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=new_dataset) o = Optimizer(target_label, dis_gt=distribution, m=model_1, ncl=ncs, t_loader=tar_selflabel_loader, N=len(new_dataset), fc_len=fc_len) uncertainty=collections.defaultdict(list) print("Training begining~~~~~~!!!!!!!!!") for epoch in range(len(clusters)): iters_ = 300 if epoch % 1== 0 else iters if epoch % 6 == 0 and epoch !=0: target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=50) target_features = torch.stack(list(target_features_dict.values())) target_features = F.normalize(target_features, dim=1) print('==> Create pseudo labels for unlabeled target domain with') rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) plabel = [] new_dataset = [] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1:continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) + 1] tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=new_dataset) o = Optimizer(target_label, dis_gt=distribution, m=model_1, ncl=ncs, t_loader=tar_selflabel_loader, N=len(new_dataset),fc_len=fc_len) contrast.index_memory = torch.cat((torch.arange(source_classes), -1 * torch.ones(k_memory).long()), dim=0).cuda() model_1.module.classifier0_3500 = nn.Linear(2048, ncs[0] + source_classes, bias=False).cuda() model_1_ema.module.classifier0_3500 = nn.Linear(2048, ncs[0] + source_classes, bias=False).cuda() model_1.module.classifier3_0_3500 = nn.Linear(1024, ncs[0] + source_classes, bias=False).cuda() model_1_ema.module.classifier3_0_3500 = nn.Linear(1024, ncs[0] + source_classes, bias=False).cuda() print(model_1.module.classifier0_3500) # if epoch !=0: # model_1.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) # model_1_ema.module.classifier0_3500.weight.data.copy_(torch.from_numpy(normalize(target_centers,axis=1)).float().cuda()) target_label_o = o.L target_label = [list(np.asarray(target_label_o[0].data.cpu())+source_classes)] contrast.index2label = [[i for i in range(source_classes)] + target_label[0]] # change pseudo labels for i in range(len(new_dataset)): new_dataset[i] = list(new_dataset[i]) for j in range(len(ncs)): new_dataset[i][j+1] = int(target_label[j][i]) new_dataset[i] = tuple(new_dataset[i]) cc=args.choice_c#(args.choice_c+1)%len(ncs) train_loader_target = get_train_loader(dataset_target, args.height, args.width, cc, args.batch_size, args.workers, args.num_instances, iters_, new_dataset) # Optimizer params = [] flag = 1.0 # if 20<epoch<=40 or 60<epoch<=80 or 120<epoch: # flag=0.1 # else: # flag=1.0 for key, value in model_1.named_parameters(): if not value.requires_grad: print(key) continue params += [{"params": [value], "lr": args.lr*flag, "weight_decay": args.weight_decay}] optimizer = torch.optim.Adam(params) # Trainer trainer = DbscanBaseTrainer(model_1, model_1_ema, contrast, num_cluster=ncs, alpha=args.alpha, fc_len=fc_len) train_loader_target.new_epoch() train_loader_source.new_epoch() trainer.train(epoch, train_loader_target, train_loader_source, optimizer, args.choice_c, print_freq=args.print_freq, train_iters=iters_) def save_model(model_ema, is_best, best_mAP, mid): save_checkpoint({ 'state_dict': model_ema.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join(args.logs_dir, 'model' + str(mid) + '_checkpoint.pth.tar')) if epoch==20: args.eval_step=2 elif epoch==40: args.eval_step=1 if ((epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1)): mAP_1 = 0#evaluator_1.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, # cmc_flag=False) mAP_2 = evaluator_1_ema.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=False) is_best = (mAP_1 > best_mAP) or (mAP_2 > best_mAP) best_mAP = max(mAP_1, mAP_2, best_mAP) save_model(model_1, (is_best), best_mAP, 1) save_model(model_1_ema, (is_best and (mAP_1 <= mAP_2)), best_mAP, 2) print('\n * Finished epoch {:3d} model no.1 mAP: {:5.1%} model no.2 mAP: {:5.1%} best: {:5.1%}{}\n'. format(epoch, mAP_1, mAP_2, best_mAP, ' *' if is_best else '')) if __name__ == '__main__': parser = argparse.ArgumentParser(description="MMT Training") # data parser.add_argument('-st', '--dataset-source', type=str, default='market1501', choices=datasets.names()) parser.add_argument('-tt', '--dataset-target', type=str, default='dukemtmc', choices=datasets.names()) parser.add_argument('-b', '--batch-size', type=int, default=64) parser.add_argument('-j', '--workers', type=int, default=8) parser.add_argument('--choice_c', type=int, default=0) parser.add_argument('--num-clusters', type=int, default=700) parser.add_argument('--ncs', type=str, default='60') parser.add_argument('--k1', type=int, default=30, help="hyperparameter for jaccard distance") parser.add_argument('--k2', type=int, default=6, help="hyperparameter for jaccard distance") parser.add_argument('--height', type=int, default=256, help="input height") parser.add_argument('--width', type=int, default=128, help="input width") parser.add_argument('--num-instances', type=int, default=4, help="each minibatch consist of " "(batch_size // num_instances) identities, and " "each identity has num_instances instances, " "default: 0 (NOT USE)") # model parser.add_argument('-a', '--arch', type=str, default='resnet50_multi', choices=models.names()) parser.add_argument('--features', type=int, default=0) parser.add_argument('--dropout', type=float, default=0) # optimizer parser.add_argument('--lr', type=float, default=0.00035, help="learning rate of new parameters, for pretrained " "parameters it is 10 times smaller than this") parser.add_argument('--momentum', type=float, default=0.9) parser.add_argument('--alpha', type=float, default=0.999) parser.add_argument('--moving-avg-momentum', type=float, default=0) parser.add_argument('--weight-decay', type=float, default=5e-4) parser.add_argument('--soft-ce-weight', type=float, default=0.5) parser.add_argument('--soft-tri-weight', type=float, default=0.8) parser.add_argument('--epochs', type=int, default=400) parser.add_argument('--iters', type=int, default=300) parser.add_argument('--lambda-value', type=float, default=0) # training configs parser.add_argument('--rr-gpu', action='store_true', help="use GPU for accelerating clustering") # parser.add_argument('--init-1', type=str, default='logs/personxTOpersonxval/resnet_ibn50a-pretrain-1_gem_RA//model_best.pth.tar', metavar='PATH') parser.add_argument('--init-1', type=str, default='logs/market1501TOdukemtmc/resnet50-pretrain-1005/model_best.pth.tar', metavar='PATH') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--print-freq', type=int, default=100) parser.add_argument('--eval-step', type=int, default=5) parser.add_argument('--n-jobs', type=int, default=8) # path working_dir = osp.dirname(osp.abspath(__file__)) parser.add_argument('--data-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'data')) parser.add_argument('--logs-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'logs/d2m_baseline/tmp')) parser.add_argument('--lambda-tri', type=float, default=1.0) parser.add_argument('--lambda-reg', type=float, default=1.0) parser.add_argument('--lambda-ct', type=float, default=0.05) parser.add_argument('--uncer-mode', type=float, default=0)#0 mean 1 max 2 min print("======mmt_train_dbscan_self-labeling=======") main()

py

UDAStrongBaseline

UDAStrongBaseline-master/source_pretrain.py

from __future__ import print_function, absolute_import import argparse import os.path as osp import random import numpy as np import sys import torch from torch import nn from torch.backends import cudnn from torch.utils.data import DataLoader from UDAsbs import datasets from UDAsbs import models from UDAsbs.trainers import PreTrainer, PreTrainer_multi from UDAsbs.evaluators import Evaluator from UDAsbs.utils.data import IterLoader from UDAsbs.utils.data import transforms as T from UDAsbs.utils.data.sampler import RandomMultipleGallerySampler from UDAsbs.utils.data.preprocessor import Preprocessor from UDAsbs.utils.logging import Logger from UDAsbs.utils.serialization import load_checkpoint, save_checkpoint, copy_state_dict from UDAsbs.utils.lr_scheduler import WarmupMultiStepLR start_epoch = best_mAP = 0 def get_data(name, data_dir, height, width, batch_size, workers, num_instances, iters=200): root = osp.join(data_dir) dataset = datasets.create(name, root) normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_set = dataset.train num_classes = dataset.num_train_pids train_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), # T.AugMix(), T.ToTensor(), normalizer ]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer ]) rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomMultipleGallerySampler(train_set, num_instances) else: sampler = None train_loader = IterLoader( DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer), batch_size=batch_size, num_workers=workers, sampler=sampler, shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters) test_loader = DataLoader( Preprocessor(list(set(dataset.query) | set(dataset.gallery)), root=dataset.images_dir, transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return dataset, num_classes, train_loader, test_loader def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True main_worker(args) def main_worker(args): global start_epoch, best_mAP cudnn.benchmark = True if not args.evaluate: sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt')) else: log_dir = osp.dirname(args.resume) sys.stdout = Logger(osp.join(log_dir, 'log_test.txt')) print("==========\nArgs:{}\n==========".format(args)) # Create data loaders iters = args.iters if (args.iters>0) else None dataset_source, num_classes, train_loader_source, test_loader_source = \ get_data(args.dataset_source, args.data_dir, args.height, args.width, args.batch_size, args.workers, args.num_instances, iters) dataset_target, _, train_loader_target, test_loader_target = \ get_data(args.dataset_target, args.data_dir, args.height, args.width, args.batch_size, args.workers, 0, iters) # Create model model = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=[num_classes]) model.cuda() model = nn.DataParallel(model) print(model) # Load from checkpoint if args.resume: checkpoint = load_checkpoint(args.resume) copy_state_dict(checkpoint['state_dict'], model) start_epoch = checkpoint['epoch'] best_mAP = checkpoint['best_mAP'] print("=> Start epoch {} best mAP {:.1%}" .format(start_epoch, best_mAP)) # Evaluator evaluator = Evaluator(model) # args.evaluate=True if args.evaluate: print("Test on source domain:") evaluator.evaluate(test_loader_source, dataset_source.query, dataset_source.gallery, cmc_flag=True, rerank=args.rerank) print("Test on target domain:") evaluator.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=True, rerank=args.rerank) return params = [] for key, value in model.named_parameters(): if not value.requires_grad: continue params += [{"params": [value], "lr": args.lr, "weight_decay": args.weight_decay}] optimizer = torch.optim.Adam(params) lr_scheduler = WarmupMultiStepLR(optimizer, args.milestones, gamma=0.1, warmup_factor=0.01, warmup_iters=args.warmup_step) # Trainer trainer = PreTrainer(model, num_classes, margin=args.margin) if 'multi' not in args.arch else PreTrainer_multi(model, num_classes, margin=args.margin) # Start training for epoch in range(start_epoch, args.epochs): train_loader_source.new_epoch() train_loader_target.new_epoch() trainer.train(epoch, train_loader_source, train_loader_target, optimizer, train_iters=len(train_loader_source), print_freq=args.print_freq) lr_scheduler.step() if ((epoch+1)%args.eval_step==0 or (epoch==args.epochs-1)): _, mAP = evaluator.evaluate(test_loader_source, dataset_source.query, dataset_source.gallery, cmc_flag=True) is_best = mAP > best_mAP best_mAP = max(mAP, best_mAP) save_checkpoint({ 'state_dict': model.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar')) print('\n * Finished epoch {:3d} source mAP: {:5.1%} best: {:5.1%}{}\n'. format(epoch, mAP, best_mAP, ' *' if is_best else '')) print("Test on target domain:") evaluator.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=True, rerank=args.rerank) if __name__ == '__main__': parser = argparse.ArgumentParser(description="Pre-training on the source domain") # data parser.add_argument('-ds', '--dataset-source', type=str, default='market1501', choices=datasets.names()) parser.add_argument('-dt', '--dataset-target', type=str, default='dukemtmc', choices=datasets.names()) parser.add_argument('-b', '--batch-size', type=int, default=64) parser.add_argument('-j', '--workers', type=int, default=4) parser.add_argument('--height', type=int, default=256, help="input height") parser.add_argument('--width', type=int, default=128, help="input width") parser.add_argument('--num-instances', type=int, default=4, help="each minibatch consist of " "(batch_size // num_instances) identities, and " "each identity has num_instances instances, " "default: 0 (NOT USE)") # model parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.names()) parser.add_argument('--features', type=int, default=0) parser.add_argument('--dropout', type=float, default=0) # optimizer parser.add_argument('--lr', type=float, default=0.00035, help="learning rate of new parameters, for pretrained ") parser.add_argument('--momentum', type=float, default=0.9) parser.add_argument('--weight-decay', type=float, default=5e-4) parser.add_argument('--warmup-step', type=int, default=10) parser.add_argument('--milestones', nargs='+', type=int, default=[40, 70], help='milestones for the learning rate decay') # training configs parser.add_argument('--resume', type=str, default="", metavar='PATH') #logs/market1501TOdukemtmc/resnet50-pretrain-1_gempooling/model_best.pth.tar parser.add_argument('--evaluate', action='store_true', help="evaluation only") parser.add_argument('--eval-step', type=int, default=40) parser.add_argument('--rerank', action='store_true', help="evaluation only") parser.add_argument('--epochs', type=int, default=80) parser.add_argument('--iters', type=int, default=200) parser.add_argument('--seed', type=int, default=1) parser.add_argument('--print-freq', type=int, default=100) parser.add_argument('--margin', type=float, default=0.0, help='margin for the triplet loss with batch hard') # path working_dir = osp.dirname(osp.abspath(__file__)) parser.add_argument('--data-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'data')) parser.add_argument('--logs-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'logs')) main()

py

UDAStrongBaseline

UDAStrongBaseline-master/sbs_trainkmeans.py

from __future__ import print_function, absolute_import import argparse import os import os.path as osp import random import numpy as np import sys from sklearn.cluster import DBSCAN,KMeans # from sklearn.preprocessing import normalize import torch from torch import nn from torch.backends import cudnn from torch.utils.data import DataLoader import torch.nn.functional as F # from torch.nn import init from UDAsbs import datasets, sinkhornknopp as sk from UDAsbs import models from UDAsbs.trainers import DbscanBaseTrainer from UDAsbs.evaluators import Evaluator, extract_features from UDAsbs.utils.data import IterLoader from UDAsbs.utils.data import transforms as T from UDAsbs.utils.data.sampler import RandomMultipleGallerySampler from UDAsbs.utils.data.preprocessor import Preprocessor from UDAsbs.utils.logging import Logger from UDAsbs.utils.serialization import load_checkpoint, save_checkpoint#, copy_state_dict from UDAsbs.models.memory_bank import onlinememory from UDAsbs.utils.faiss_rerank import compute_jaccard_distance # import ipdb from UDAsbs.models.dsbn import convert_dsbn from torch.nn import Parameter import faiss import collections start_epoch = best_mAP = 0 def get_data(name, data_dir, l=1, shuffle=False): root = osp.join(data_dir) dataset = datasets.create(name, root, l) label_dict = {} for i, item_l in enumerate(dataset.train): if shuffle: labels= tuple([0 for i in range(l)]) dataset.train[i]=(item_l[0],)+labels+(item_l[-1],) if item_l[1] in label_dict: label_dict[item_l[1]].append(i) else: label_dict[item_l[1]] = [i] return dataset, label_dict def get_train_loader(dataset, height, width, choice_c, batch_size, workers, num_instances, iters, trainset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) train_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.RandomHorizontalFlip(p=0.5), T.Pad(10), T.RandomCrop((height, width)), T.ToTensor(), normalizer, T.RandomErasing(probability=0.5, mean=[0.596, 0.558, 0.497]) ]) train_set = trainset #dataset.train if trainset is None else trainset rmgs_flag = num_instances > 0 if rmgs_flag: sampler = RandomMultipleGallerySampler(train_set, num_instances, choice_c) else: sampler = None train_loader = IterLoader( DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer, mutual=True), batch_size=batch_size, num_workers=workers, sampler=sampler, shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters) return train_loader def get_test_loader(dataset, height, width, batch_size, workers, testset=None): normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]) test_transformer = T.Compose([ T.Resize((height, width), interpolation=3), T.ToTensor(), normalizer ]) if (testset is None): testset = list(set(dataset.query) | set(dataset.gallery)) test_loader = DataLoader( Preprocessor(testset, root=dataset.images_dir, transform=test_transformer), batch_size=batch_size, num_workers=workers, shuffle=False, pin_memory=True) return test_loader def copy_state_dict(state_dict, model, strip=None): tgt_state = model.state_dict() copied_names = set() for name, param in state_dict.items(): name = name.replace('module.', '') if strip is not None and name.startswith(strip): name = name[len(strip):] if name not in tgt_state: continue if isinstance(param, Parameter): param = param.data if param.size() != tgt_state[name].size(): print('mismatch:', name, param.size(), tgt_state[name].size()) continue tgt_state[name].copy_(param) copied_names.add(name) missing = set(tgt_state.keys()) - copied_names if len(missing) > 0: print("missing keys in state_dict:", missing) return model def create_model(args, ncs, wopre=False): model_1 = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) model_1_ema = models.create(args.arch, num_features=args.features, dropout=args.dropout, num_classes=ncs) if not wopre: initial_weights = load_checkpoint(args.init_1) copy_state_dict(initial_weights['state_dict'], model_1) copy_state_dict(initial_weights['state_dict'], model_1_ema) print('load pretrain model:{}'.format(args.init_1)) # adopt domain-specific BN convert_dsbn(model_1) convert_dsbn(model_1_ema) model_1.cuda() model_1_ema.cuda() model_1 = nn.DataParallel(model_1) model_1_ema = nn.DataParallel(model_1_ema) for i, cl in enumerate(ncs): exec('model_1_ema.module.classifier{}_{}.weight.data.copy_(model_1.module.classifier{}_{}.weight.data)'.format(i,cl,i,cl)) return model_1, model_1_ema def main(): args = parser.parse_args() if args.seed is not None: random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True main_worker(args) class Optimizer: def __init__(self, target_label, m, dis_gt, t_loader,N, hc=3, ncl=None, n_epochs=200, weight_decay=1e-5, ckpt_dir='/'): self.num_epochs = n_epochs self.momentum = 0.9 self.weight_decay = weight_decay self.checkpoint_dir = ckpt_dir self.N=N self.resume = True self.checkpoint_dir = None self.writer = None # model stuff self.hc = len(ncl)#10 self.K = ncl#3000 self.model = m self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.L = [torch.LongTensor(target_label[i]).to(self.dev) for i in range(len(self.K))] self.nmodel_gpus = 4#len() self.pseudo_loader = t_loader#torch.utils.data.DataLoader(t_loader,batch_size=256) # can also be DataLoader with less aug. self.train_loader = t_loader self.lamb = 25#args.lamb # the parameter lambda in the SK algorithm self.cpu=True self.dis_gt=dis_gt dtype_='f64' if dtype_ == 'f32': self.dtype = torch.float32 if not self.cpu else np.float32 else: self.dtype = torch.float64 if not self.cpu else np.float64 self.outs = self.K # activations of previous to last layer to be saved if using multiple heads. self.presize = 2048 def optimize_labels(self): if self.cpu: sk.cpu_sk(self) else: sk.gpu_sk(self) self.PS = 0 return self.L def print_cluster_acc(label_dict,target_label_tmp): num_correct = 0 for pid in label_dict: pid_index = np.asarray(label_dict[pid]) pred_label = np.argmax(np.bincount(target_label_tmp[pid_index])) num_correct += (target_label_tmp[pid_index] == pred_label).astype(np.float32).sum() cluster_accuracy = num_correct / len(target_label_tmp) print(f'cluster accucary: {cluster_accuracy:.3f}') def main_worker(args): global start_epoch, best_mAP cudnn.benchmark = True sys.stdout = Logger(osp.join(args.logs_dir, 'log{}.txt'.format(args.cluster_iter))) print("==========\nArgs:{}\n==========".format(args)) iters = args.iters if (args.iters > 0) else None ncs = [int(x) for x in args.ncs.split(',')] if args.cluster_iter==10: args.epochs = 80 # Create data loaders dataset_target, label_dict = get_data(args.dataset_target, args.data_dir, len(ncs),True) test_loader_target = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers) tar_cluster_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, testset=dataset_target.train) dataset_source, _ = get_data(args.dataset_source, args.data_dir, len(ncs)) sour_cluster_loader = get_test_loader(dataset_source, args.height, args.width, args.batch_size, args.workers, testset=dataset_source.train) train_loader_source = get_train_loader(dataset_source, args.height, args.width, 0, args.batch_size, args.workers, args.num_instances, args.iters, dataset_source.train) model_1, model_1_ema = create_model(args, [fc_len for fc_len in ncs]) target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=100) target_features = F.normalize(torch.stack(list(target_features_dict.values())), dim=1) # Calculate distance print('==> Create pseudo labels for unlabeled target domain') cluster_name='kmeans' if cluster_name=='dbscan': rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) del target_features # DBSCAN cluster eps = 0.6 # 0.6 print('Clustering criterion: eps: {:.3f}'.format(eps)) cluster = DBSCAN(eps=eps, min_samples=4, metric='precomputed', n_jobs=-1) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) # num_ids = len(set(pseudo_labels)) - (1 if -1 in pseudo_labels else 0) plabel=[] new_dataset=[] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1: continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) +1] print('new class are {}, length of new dataset is {}'.format(ncs, len(new_dataset))) else: prenc_i = -1 moving_avg_features = target_features.numpy() target_label = [] for nc_i in ncs: plabel_path = os.path.join(args.logs_dir,'target_label{}_{}.npy'.format(nc_i, args.cluster_iter)) if os.path.exists(plabel_path): target_label_tmp = np.load(plabel_path) print('\n {} existing\n'.format(plabel_path)) else: if prenc_i == nc_i: target_label.append(target_label_tmp) print_cluster_acc(label_dict, target_label_tmp) continue # km = KMeans(n_clusters=nc_i, random_state=args.seed, n_jobs=args.n_jobs).fit(moving_avg_features) # target_label_tmp = np.asarray(km.labels_) # cluster_centers = np.asarray(km.cluster_centers_) cluster = faiss.Kmeans(2048, nc_i, niter=300, verbose=True, gpu=True) cluster.train(moving_avg_features) _, labels = cluster.index.search(moving_avg_features, 1) target_label_tmp = labels.reshape(-1) target_label.append(target_label_tmp) print_cluster_acc(label_dict, target_label_tmp) prenc_i=nc_i new_dataset = dataset_target.train # Initialize source-domain class centroids print("==> Initialize source-domain class centroids in the hybrid memory") source_features, _ = extract_features(model_1, sour_cluster_loader, print_freq=50) sour_fea_dict = collections.defaultdict(list) print("==> Ending source-domain class centroids in the hybrid memory") for item in sorted(dataset_source.train): f=item[0] pid=item[1] sour_fea_dict[pid].append(source_features[f].unsqueeze(0)) source_centers = [torch.cat(sour_fea_dict[pid], 0).mean(0) for pid in sorted(sour_fea_dict.keys())] source_centers = torch.stack(source_centers, 0) source_centers = F.normalize(source_centers, dim=1) del sour_fea_dict, source_features, sour_cluster_loader # Evaluator evaluator_1 = Evaluator(model_1) evaluator_1_ema = Evaluator(model_1_ema) source_classes = dataset_source.num_train_pids k_memory=8192 contrast = onlinememory(2048, sour_numclass=source_classes,K=k_memory+source_classes, index2label=target_label, choice_c=args.choice_c, T=0.07, use_softmax=True).cuda() contrast.index_memory = torch.cat((torch.arange(source_classes), -1*torch.ones(k_memory).long()), dim=0).cuda() contrast.memory = torch.cat((source_centers, torch.rand(k_memory, 2048)), dim=0).cuda() skin=True if skin: tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers,testset=new_dataset) else: tar_selflabel_loader=None o = Optimizer(target_label, dis_gt=None, m=model_1_ema, ncl=ncs, t_loader=tar_selflabel_loader, N=len(new_dataset)) print("Training begining~~~~~~!!!!!!!!!") for epoch in range(args.epochs): iters_ = 300 if epoch % 1== 0 else iters # if epoch % 6 == 0 and epoch != 0: if epoch == args.epochs - 1: prenc_i=-1 target_features_dict, _ = extract_features(model_1_ema, tar_cluster_loader, print_freq=50) target_features = torch.stack(list(target_features_dict.values())) # torch.cat([target_features[f[0]].unsqueeze(0) for f in dataset_target.train], 0) target_features = F.normalize(target_features, dim=1) for in_, nc_i in enumerate(ncs): if cluster_name == 'dbscan': print('==> Create pseudo labels for unlabeled target domain with') rerank_dist = compute_jaccard_distance(target_features, k1=args.k1, k2=args.k2) # select & cluster images as training set of this epochs pseudo_labels = cluster.fit_predict(rerank_dist) plabel = [] new_dataset = [] for i, (item, label) in enumerate(zip(dataset_target.train, pseudo_labels)): if label == -1: continue plabel.append(label) new_dataset.append((item[0], label, item[-1])) target_label = [plabel] ncs = [len(set(plabel)) + 1] print('new class are {}, length of new dataset is {}'.format(ncs, len(new_dataset))) else: if prenc_i == nc_i: continue print('\n Clustering into {} classes \n'.format(nc_i)) moving_avg_features = target_features.numpy() km = KMeans(n_clusters=nc_i, random_state=args.seed, n_jobs=args.n_jobs).fit(moving_avg_features) target_label_tmp = np.asarray(km.labels_) cluster_centers = np.asarray(km.cluster_centers_) # cluster = faiss.Kmeans(2048, nc_i, niter=300, verbose=True, gpu=True) # cluster.train(moving_avg_features) # _, labels = cluster.index.search(moving_avg_features, 1) # target_label_tmp = labels.reshape(-1) np.save("{}/target_label{}_{}.npy".format(args.logs_dir, nc_i, args.cluster_iter + 1), target_label_tmp) # cluster_centers = cluster.centroids print_cluster_acc(label_dict, target_label_tmp) dev = torch.device("cuda" if torch.cuda.is_available() else "cpu") o.L[in_] = torch.LongTensor(target_label_tmp).to(dev) prenc_i = nc_i break # tar_selflabel_loader = get_test_loader(dataset_target, args.height, args.width, args.batch_size, args.workers, # testset=new_dataset) # o = Optimizer(target_label, dis_gt=None, m=model_1, ncl=ncs, # t_loader=tar_selflabel_loader, N=len(new_dataset),fc_len=fc_len) contrast.index_memory = torch.cat((torch.arange(source_classes), -1 * torch.ones(k_memory).long()), dim=0).cuda() target_label_o = o.L target_label = [np.asarray(target_label_o[i].data.cpu()) for i in range(len(ncs))] target_label_mb = [list(np.asarray(target_label_o[i].data.cpu())+source_classes) for i in range(len(ncs))] contrast.index2label = [[i for i in range(source_classes)] + target_label_mb[i] for i in range(len(ncs))] for i in range(len(new_dataset)): new_dataset[i] = list(new_dataset[i]) for j in range(len(ncs)): new_dataset[i][j+1] = int(target_label[j][i]) new_dataset[i] = tuple(new_dataset[i]) #cc =(args.choice_c+1)%len(ncs) train_loader_target = get_train_loader(dataset_target, args.height, args.width, args.choice_c, args.batch_size, args.workers, args.num_instances, iters_, new_dataset) # Optimizer params = [] if 40<epoch<=70:flag=0.1 elif 70<epoch<=80:flag = 0.01 else:flag=1.0 for key, value in model_1.named_parameters(): if not value.requires_grad: print(key) continue params += [{"params": [value], "lr": args.lr*flag, "weight_decay": args.weight_decay}] optimizer = torch.optim.Adam(params) # Trainer trainer = DbscanBaseTrainer(model_1, model_1_ema, contrast, num_cluster=ncs, alpha=args.alpha) train_loader_target.new_epoch() train_loader_source.new_epoch() trainer.train(epoch, train_loader_target, train_loader_source, optimizer, args.choice_c, print_freq=args.print_freq, train_iters=iters_) o.optimize_labels() def save_model(model_ema, is_best, best_mAP, mid): save_checkpoint({ 'state_dict': model_ema.state_dict(), 'epoch': epoch + 1, 'best_mAP': best_mAP, }, is_best, fpath=osp.join(args.logs_dir, 'model' + str(mid) + '_checkpoint.pth.tar')) if epoch==20: args.eval_step=2 elif epoch==50: args.eval_step=1 if ((epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1)): mAP_1 = 0#evaluator_1.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, # cmc_flag=False) mAP_2 = evaluator_1_ema.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=False) is_best = (mAP_1 > best_mAP) or (mAP_2 > best_mAP) best_mAP = max(mAP_1, mAP_2, best_mAP) save_model(model_1, (is_best), best_mAP, 1) save_model(model_1_ema, (is_best and (mAP_1 <= mAP_2)), best_mAP, 2) print('\n * Finished epoch {:3d} model no.1 mAP: {:5.1%} model no.2 mAP: {:5.1%} best: {:5.1%}{}\n'. format(epoch, mAP_1, mAP_2, best_mAP, ' *' if is_best else '')) print('Test on the best model.') checkpoint = load_checkpoint(osp.join(args.logs_dir, 'model_best.pth.tar')) model_1_ema.load_state_dict(checkpoint['state_dict']) evaluator_1_ema.evaluate(test_loader_target, dataset_target.query, dataset_target.gallery, cmc_flag=True) if __name__ == '__main__': parser = argparse.ArgumentParser(description="MMT Training") # data parser.add_argument('-st', '--dataset-source', type=str, default='market1501', choices=datasets.names()) parser.add_argument('-tt', '--dataset-target', type=str, default='dukemtmc', choices=datasets.names()) parser.add_argument('-b', '--batch-size', type=int, default=64) parser.add_argument('-j', '--workers', type=int, default=8) parser.add_argument('--choice_c', type=int, default=0) parser.add_argument('--num-clusters', type=int, default=-1, help='discard') parser.add_argument('--cluster-iter', type=int, default=10) parser.add_argument('--ncs', type=str, default='600,700,800') parser.add_argument('--k1', type=int, default=30, help="hyperparameter for jaccard distance") parser.add_argument('--k2', type=int, default=6, help="hyperparameter for jaccard distance") parser.add_argument('--height', type=int, default=256, help="input height") parser.add_argument('--width', type=int, default=128, help="input width") parser.add_argument('--num-instances', type=int, default=4, help="each minibatch consist of " "(batch_size // num_instances) identities, and " "each identity has num_instances instances, " "default: 0 (NOT USE)") # model parser.add_argument('-a', '--arch', type=str, default='resnet50_multi', choices=models.names()) parser.add_argument('--features', type=int, default=0) parser.add_argument('--dropout', type=float, default=0) # optimizer parser.add_argument('--lr', type=float, default=0.00035, help="learning rate of new parameters, for pretrained " "parameters it is 10 times smaller than this") parser.add_argument('--momentum', type=float, default=0.9) parser.add_argument('--alpha', type=float, default=0.999) parser.add_argument('--moving-avg-momentum', type=float, default=0) parser.add_argument('--weight-decay', type=float, default=5e-4) parser.add_argument('--soft-ce-weight', type=float, default=0.5) parser.add_argument('--soft-tri-weight', type=float, default=0.8) parser.add_argument('--epochs', type=int, default=400) parser.add_argument('--iters', type=int, default=300) parser.add_argument('--lambda-value', type=float, default=0) # training configs parser.add_argument('--rr-gpu', action='store_true', help="use GPU for accelerating clustering") parser.add_argument('--init-1', type=str, default='logs/market1501TOdukemtmc/resnet50-pretrain-1005/model_best.pth.tar', metavar='PATH') parser.add_argument('--seed', type=int, default=1) parser.add_argument('--print-freq', type=int, default=100) parser.add_argument('--eval-step', type=int, default=5) parser.add_argument('--n-jobs', type=int, default=8) # path working_dir = osp.dirname(osp.abspath(__file__)) parser.add_argument('--data-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'data')) parser.add_argument('--logs-dir', type=str, metavar='PATH', default=osp.join(working_dir, 'logs/d2m_baseline/tmp')) parser.add_argument('--lambda-tri', type=float, default=1.0) parser.add_argument('--lambda-reg', type=float, default=1.0) parser.add_argument('--lambda-ct', type=float, default=1.0) parser.add_argument('--uncer-mode', type=float, default=0, help='0 mean, 1 max, 2 min') print("======mmt_train_dbscan_self-labeling=======") main()

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/evaluators.py

from __future__ import print_function, absolute_import import time from collections import OrderedDict import numpy as np import torch from .evaluation_metrics import cmc, mean_ap from .feature_extraction import extract_cnn_feature from .utils.meters import AverageMeter from .utils.rerank import re_ranking def extract_features(model, data_loader, choice_c=0, adaibn=False, print_freq=100, metric=None): # if adaibn==True: # model.train() # for i, item in enumerate(data_loader): # imgs, fnames, pids = item[0], item[1], item[choice_c + 2] # outputs = model(imgs) # if (i + 1) % print_freq == 0: # print('Extract Features: [{}/{}]\t' # .format(i + 1, len(data_loader))) model.eval() batch_time = AverageMeter() data_time = AverageMeter() features = OrderedDict() labels = OrderedDict() end = time.time() with torch.no_grad(): for i, item in enumerate(data_loader): imgs, fnames, pids =item[0], item[1], item[choice_c+2] data_time.update(time.time() - end) outputs = extract_cnn_feature(model, imgs) for fname, output, pid in zip(fnames, outputs, pids): features[fname] = output labels[fname] = pid batch_time.update(time.time() - end) end = time.time() if (i + 1) % print_freq == 0: print('Extract Features: [{}/{}]\t' 'Time {:.3f} ({:.3f})\t' 'Data {:.3f} ({:.3f})\t' .format(i + 1, len(data_loader), batch_time.val, batch_time.avg, data_time.val, data_time.avg)) return features, labels def pairwise_distance(features, query=None, gallery=None, metric=None): if query is None and gallery is None: n = len(features) x = torch.cat(list(features.values())) x = x.view(n, -1) if metric is not None: x = metric.transform(x) dist_m = torch.pow(x, 2).sum(dim=1, keepdim=True) * 2 dist_m = dist_m.expand(n, n) - 2 * torch.mm(x, x.t()) return dist_m x = torch.cat([features[item[0]].unsqueeze(0) for item in query], 0) y = torch.cat([features[item[0]].unsqueeze(0) for item in gallery], 0) m, n = x.size(0), y.size(0) x = x.view(m, -1) y = y.view(n, -1) if metric is not None: x = metric.transform(x) y = metric.transform(y) dist_m = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n) + \ torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t() dist_m.addmm_(1, -2, x, y.t()) return dist_m, x.numpy(), y.numpy() from .utils import to_numpy def submission_visUDA(distmat,query_ids,gallery_ids,query,gallery): #TODO query_name2index={} with open("/home/zhengkecheng3/data/reid/challenge_datasets/index_validation_query.txt", 'r') as f: # with语句自动调用close()方法 line = f.readline() while line: eachline = line.split() query_name2index[eachline[0]]=eachline[-1] line = f.readline() gallery_name2index = {} with open("/home/zhengkecheng3/data/reid/challenge_datasets/index_validation_gallery.txt", 'r') as f: line = f.readline() while line: eachline = line.split() gallery_name2index[eachline[0]] = eachline[-1] line = f.readline() distmat = to_numpy(distmat) indices = np.argsort(distmat, axis=1) result={} for i,x in enumerate(query_ids): result[str(x)]=indices[i,:100] with open('result.txt','w') as f: for i in range(len(query_ids)): indexs=result[str(i)] out_str="" for j in indexs: item_now=(4-len(str(j)))*'0'+str(j) out_str=out_str+item_now+" " f.write(out_str[:-1]+'\n') print(result) def evaluate_all(query_features, gallery_features, distmat, query=None, gallery=None, query_ids=None, gallery_ids=None, query_cams=None, gallery_cams=None, cmc_topk=(1, 5, 10), cmc_flag=False): if query is not None and gallery is not None: query_ids = [item[1] for item in query] gallery_ids = [item[1] for item in gallery] query_cams = [item[-1] for item in query] gallery_cams = [item[-1] for item in gallery] else: assert (query_ids is not None and gallery_ids is not None and query_cams is not None and gallery_cams is not None) # submission_visUDA(distmat, query_ids, gallery_ids,query,gallery) # Compute mean AP mAP = mean_ap(distmat, query_ids, gallery_ids, query_cams, gallery_cams) print('Mean AP: {:4.1%}'.format(mAP)) cmc_configs = { 'market1501': dict(separate_camera_set=False, single_gallery_shot=False, first_match_break=True) } cmc_scores = {name: cmc(distmat, query_ids, gallery_ids, query_cams, gallery_cams, **params) for name, params in cmc_configs.items()} print('CMC Scores:') for k in cmc_topk: print(' top-{:<4}{:12.1%}' .format(k, cmc_scores['market1501'][k-1])) if (not cmc_flag): return mAP return cmc_scores['market1501'][0], mAP class Evaluator(object): def __init__(self, model): super(Evaluator, self).__init__() self.model = model def evaluate(self, data_loader, query, gallery, metric=None, cmc_flag=False, rerank=False, pre_features=None): if (pre_features is None): features, _ = extract_features(self.model, data_loader) else: features = pre_features distmat, query_features, gallery_features = pairwise_distance(features, query, gallery, metric=metric) if (not rerank): results = evaluate_all(query_features, gallery_features, distmat, query=query, gallery=gallery, cmc_flag=cmc_flag) return results print('Applying person re-ranking ...') distmat_qq,_,_ = pairwise_distance(features, query, query, metric=metric) distmat_gg,_,_ = pairwise_distance(features, gallery, gallery, metric=metric) distmat = re_ranking(distmat.numpy(), distmat_qq.numpy(), distmat_gg.numpy()) return evaluate_all(query_features, gallery_features, distmat, query=query, gallery=gallery, cmc_flag=cmc_flag)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/trainers.py

from __future__ import print_function, absolute_import import time import torch import torch.nn as nn from torch.nn import functional as F from .evaluation_metrics import accuracy from .loss import SoftTripletLoss_vallia, CrossEntropyLabelSmooth, SoftTripletLoss, SoftEntropy from .memorybank.NCECriterion import MultiSoftmaxLoss, NCECriterion, NCESoftmaxLoss from .utils.meters import AverageMeter class PreTrainer_multi(object): def __init__(self, model, num_classes, margin=0.0): super(PreTrainer_multi, self).__init__() self.model = model self.criterion_ce = CrossEntropyLabelSmooth(num_classes).cuda() self.criterion_triple = SoftTripletLoss_vallia(margin=margin).cuda() def train(self, epoch, data_loader_source, data_loader_target, optimizer, train_iters=200, print_freq=1): self.model.train() batch_time = AverageMeter() data_time = AverageMeter() losses_ce = AverageMeter() losses_tr = AverageMeter() precisions = AverageMeter() losses_ce_3 = AverageMeter() losses_tr_3 = AverageMeter() precisions_3 = AverageMeter() end = time.time() for i in range(train_iters): # import ipdb # ipdb.set_trace() source_inputs = data_loader_source.next() target_inputs = data_loader_target.next() data_time.update(time.time() - end) s_inputs, targets = self._parse_data(source_inputs) t_inputs, _ = self._parse_data(target_inputs) s_features, s_cls_out,_,_,s_cls_out_3,s_features_3 = self.model(s_inputs,training=True) # target samples: only forward self.model(t_inputs,training=True) # backward main # loss_ce, loss_tr, prec1 = self._forward(s_features, s_cls_out[0], targets) loss_ce_3, loss_tr_3, prec1_3 = self._forward(s_features_3, s_cls_out_3[0], targets) loss = loss_ce + loss_tr + loss_ce_3 + loss_tr_3 losses_ce.update(loss_ce.item()) losses_tr.update(loss_tr.item()) precisions.update(prec1) losses_ce_3.update(loss_ce_3.item()) losses_tr_3.update(loss_tr_3.item()) precisions_3.update(prec1_3) optimizer.zero_grad() loss.backward() optimizer.step() batch_time.update(time.time() - end) end = time.time() if ((i + 1) % print_freq == 0): print('Epoch: [{}][{}/{}]\t' 'Time {:.3f} ({:.3f})\t' 'Data {:.3f} ({:.3f})\t' 'Loss_ce {:.3f} ({:.3f})\t' 'Loss_tr {:.3f} ({:.3f})\t' 'Prec {:.2%} ({:.2%})\t' 'Loss_ce_3 {:.3f} ({:.3f})\t' 'Loss_tr_3 {:.3f} ({:.3f})\t' 'Prec_3 {:.2%} ({:.2%})' .format(epoch, i + 1, train_iters, batch_time.val, batch_time.avg, data_time.val, data_time.avg, losses_ce.val, losses_ce.avg, losses_tr.val, losses_tr.avg, precisions.val, precisions.avg, losses_ce_3.val, losses_ce_3.avg, losses_tr_3.val, losses_tr_3.avg, precisions_3.val, precisions_3.avg)) def _parse_data(self, inputs): imgs, _, pids,_, _ = inputs#, pids, index inputs = imgs.cuda() targets = pids.cuda() return inputs, targets def _forward(self, s_features, s_outputs, targets): loss_ce = self.criterion_ce(s_outputs, targets) loss_tr = self.criterion_triple(s_features, s_features, targets) prec, = accuracy(s_outputs.data, targets.data) prec = prec[0] return loss_ce, loss_tr, prec class PreTrainer(object): def __init__(self, model, num_classes, margin=0.0): super(PreTrainer, self).__init__() self.model = model self.criterion_ce = CrossEntropyLabelSmooth(num_classes).cuda() self.criterion_triple = SoftTripletLoss_vallia(margin=margin).cuda() def train(self, epoch, data_loader_source, data_loader_target, optimizer, train_iters=200, print_freq=1): self.model.train() batch_time = AverageMeter() data_time = AverageMeter() losses_ce = AverageMeter() losses_tr = AverageMeter() precisions = AverageMeter() end = time.time() for i in range(train_iters): # import ipdb # ipdb.set_trace() source_inputs = data_loader_source.next() target_inputs = data_loader_target.next() data_time.update(time.time() - end) s_inputs, targets = self._parse_data(source_inputs) t_inputs, _ = self._parse_data(target_inputs) s_features, s_cls_out,_,_ = self.model(s_inputs,training=True) # target samples: only forward _,_,_,_= self.model(t_inputs,training=True) # backward main # loss_ce, loss_tr, prec1 = self._forward(s_features, s_cls_out[0], targets) loss = loss_ce + loss_tr losses_ce.update(loss_ce.item()) losses_tr.update(loss_tr.item()) precisions.update(prec1) optimizer.zero_grad() loss.backward() optimizer.step() batch_time.update(time.time() - end) end = time.time() if ((i + 1) % print_freq == 0): print('Epoch: [{}][{}/{}]\t' 'Time {:.3f} ({:.3f})\t' 'Data {:.3f} ({:.3f})\t' 'Loss_ce {:.3f} ({:.3f})\t' 'Loss_tr {:.3f} ({:.3f})\t' 'Prec {:.2%} ({:.2%})' .format(epoch, i + 1, train_iters, batch_time.val, batch_time.avg, data_time.val, data_time.avg, losses_ce.val, losses_ce.avg, losses_tr.val, losses_tr.avg, precisions.val, precisions.avg)) def _parse_data(self, inputs): imgs, _, pids,_, _ = inputs#, pids, index inputs = imgs.cuda() targets = pids.cuda() return inputs, targets def _forward(self, s_features, s_outputs, targets): loss_ce = self.criterion_ce(s_outputs, targets) loss_tr = self.criterion_triple(s_features, s_features, targets) prec, = accuracy(s_outputs.data, targets.data) prec = prec[0] return loss_ce, loss_tr, prec class DbscanBaseTrainer_unc_ema(object): def __init__(self, model_1, model_1_ema, contrast, contrast_center, contrast_center_sour, num_cluster=None, c_name=None, alpha=0.999, fc_len=3000,source_classes=702,uncer_mode=0): super(DbscanBaseTrainer_unc_ema, self).__init__() self.model_1 = model_1 self.num_cluster = num_cluster self.c_name = [fc_len for _ in range(len(num_cluster))] self.model_1_ema = model_1_ema self.uncer_mode=uncer_mode self.alpha = alpha self.criterion_ce = CrossEntropyLabelSmooth(self.num_cluster[0],False).cuda() # self.criterion_tri = SoftTripletLoss(margin=0.0).cuda() self.criterion_tri_uncer = SoftTripletLoss(margin=None,uncer_mode=self.uncer_mode).cuda() self.source_classes = source_classes self.contrast = contrast # self.kl = nn.KLDivLoss() self.sm = torch.nn.Softmax(dim=1) self.log_sm = torch.nn.LogSoftmax(dim=1) # self.cross_batch=CrossBatchMemory() self.kl_distance = nn.KLDivLoss(reduction='none') def train(self, epoch, data_loader_target, data_loader_source, optimizer, choice_c, lambda_tri=1.0 , lambda_ct=1.0, lambda_reg=0.06, print_freq=100, train_iters=200, uncertainty_d=None): self.model_1.train() self.model_1_ema.train() batch_time = AverageMeter() data_time = AverageMeter() losses_ce = [AverageMeter(), AverageMeter()] losses_tri = [AverageMeter(), AverageMeter()] loss_kldiv = AverageMeter() loss_s = AverageMeter() losses_tri_unc = AverageMeter() contra_loss = AverageMeter() precisions = [AverageMeter(), AverageMeter()] end = time.time() for i in range(train_iters): target_inputs = data_loader_target.next() source_inputs = data_loader_source.next() data_time.update(time.time() - end) # process inputs items = self._parse_data(target_inputs) items_source = self._parse_data(source_inputs) inputs_1_t, inputs_2_t, index_t = items[0], items[1], items[-1] inputs_1_s, inputs_2_s, index_s = items_source[0], items_source[1], items_source[-1] inputs = self.range_spbn(inputs_1_s, inputs_1_t) f_out, p_out, memory_f, _, p_out_3, f_out_3 = self.model_1(inputs, training=True) f_out_s1, f_out_t1 = self.derange_spbn(f_out) _, p_out_t1 = self.derange_spbn(p_out[0]) _, memory_f_t1 = self.derange_spbn(memory_f) _, p_out_3_t1 = self.derange_spbn(p_out_3[0]) _, f_out_3_t1 = self.derange_spbn(f_out_3) with torch.no_grad(): f_out_ema, p_out_ema, memory_f_ema, _, p_out_3_ema, f_out_3_ema \ = self.model_1_ema(inputs, training=True) f_out_s1_ema, f_out_t1_ema = self.derange_spbn(f_out_ema) _, p_out_t1_ema = self.derange_spbn(p_out_ema[0]) _, memory_f_t1_ema = self.derange_spbn(memory_f_ema) _, p_out_3_t1_ema = self.derange_spbn(p_out_3_ema[0]) _, f_out_3_t1_ema = self.derange_spbn(f_out_3_ema) with torch.no_grad(): queue = self.contrast.memory[:self.contrast.sour_numclass, :].clone() ml_sour = torch.matmul(f_out_t1, queue.transpose(1, 0).detach()) ml_sour_ema = torch.matmul(f_out_t1_ema, queue.transpose(1, 0).detach()) ########## [memory center]-level uncertainty loss_ce_1, loss_reg, exp_variance = self.update_variance(items[2], p_out_t1, p_out_3_t1, p_out_t1_ema, p_out_3_t1_ema, ml_sour, ml_sour_ema, f_out_t1, f_out_t1_ema) loss_ce_1 = loss_ce_1#(loss_ce_1+loss_ce_1_3)/2.0 exp_variance_np=exp_variance.data.cpu().numpy() for i_num,i_un in enumerate(index_t.data.cpu().numpy()): uncertainty_d[i_un].append(exp_variance_np[i_num]) # exp_variance=torch.tensor(0) loss_kl = exp_variance.mean() contra_loss_instance, contra_loss_center, _, _ = \ self.contrast(memory_f_t1, f_out_s1, f_out_t1, f_out_t1_ema, index_t, items_source[2], exp_variance, epoch=epoch) ########## feature-level uncertainty # loss_ce_1, exp_variance = self.update_variance_self(items[2], p_out_t1, f_out_t1, f_out_t1_ema ) ########## normal ce loss loss_ce_1_norm = torch.tensor(0)#(self.criterion_ce(p_out_t1, items[2]) +self.criterion_ce(p_out_3_t1, items[2])) / 2.0 ########## uncertainty hard triplet loss loss_tri_unc = self.criterion_tri_uncer(f_out_t1, f_out_t1_ema, items[2], exp_variance) if epoch % 6 != 0: loss = loss_ce_1 + lambda_tri*loss_tri_unc + lambda_reg*loss_reg + lambda_ct*contra_loss_instance + contra_loss_center else: loss = loss_ce_1 + lambda_tri*loss_tri_unc + lambda_reg*loss_reg + contra_loss_center optimizer.zero_grad() loss.backward() optimizer.step() self._update_ema_variables(self.model_1, self.model_1_ema, self.alpha, epoch * len(data_loader_target) + i) prec_1, = accuracy(p_out_t1.data, items[choice_c + 2].data) losses_ce[0].update(loss_ce_1.item()) losses_ce[1].update(loss_ce_1_norm.item()) # losses_tri[0].update(loss_tri_1.item()) loss_s.update(contra_loss_center.item()) loss_kldiv.update(loss_kl.item()) losses_tri_unc.update(loss_tri_unc.item()) contra_loss.update(contra_loss_instance.item()) precisions[0].update(prec_1[0]) # print log # batch_time.update(time.time() - end) end = time.time() if (i + 1) % print_freq == 1: print('Epoch: [{}][{}/{}]\t' 'Time {:.3f} ({:.3f})\t' 'Data {:.3f} ({:.3f})\t' 'Loss_ce {:.3f} / {:.3f}\t' 'loss_kldiv {:.3f}\t' 'Loss_tri {:.3f} / Loss_tri_soft {:.3f} \t' 'contra_loss_center {:.3f}\t' 'contra_loss {:.3f}\t' 'Prec {:.2%} / {:.2%}\t' .format(epoch, i, len(data_loader_target), batch_time.val, batch_time.avg, data_time.val, data_time.avg, losses_ce[0].avg, losses_ce[1].avg, loss_kldiv.avg, losses_tri[0].avg, losses_tri_unc.avg, loss_s.avg, contra_loss.avg, precisions[0].avg, precisions[1].avg)) return uncertainty_d def update_variance(self, labels, pred1, pred2, pred_ema, pred2_ema, ml_sour, ml_sour_ema,f_out_t1,f_out_t1_ema): #items[2], p_out_t1, p_out_3_t1, p_out_t1_ema, ml_sour,ml_sour_ema,f_out_t1,f_out_t1_ema) loss_4layer = self.criterion_ce(pred1, labels) loss_3layer = self.criterion_ce(pred2, labels) only_sour=False if only_sour: variance = torch.sum(self.kl_distance(self.log_sm(ml_sour), self.sm(ml_sour_ema.detach())), dim=1) else: # variance = torch.sum(self.kl_distance(self.log_sm(pred2), self.sm(pred_ema.detach())), dim=1) # variance = (torch.sum(self.kl_distance(self.log_sm(ml_sour), self.sm(ml_sour_ema.detach())), dim=1) + # torch.sum(self.kl_distance(self.log_sm(pred1), self.sm(pred2_ema.detach())), dim=1)) / 2.0 variance = torch.sum(self.kl_distance(self.log_sm(torch.cat((pred2,ml_sour),1)), self.sm(torch.cat((pred2_ema,ml_sour_ema),1).detach())), dim=1) # variance = ( torch.sum(self.kl_distance(self.log_sm(torch.cat((pred1,ml_sour),1)), self.sm(torch.cat((pred2,ml_sour_ema),1).detach())), dim=1) # +torch.sum(self.kl_distance(self.log_sm(f_out_t1),self.sm(f_out_t1_ema.detach())), dim=1) )/2.0 # variance = (torch.sum(self.kl_distance(self.log_sm(torch.cat((pred1,ml_sour),1)), self.sm(torch.cat((pred2 ,ml_sour_ema),1).detach())), dim=1)+\ # torch.sum(self.kl_distance(self.log_sm(torch.cat((pred1,ml_sour),1)), self.sm(torch.cat((pred_ema,ml_sour_ema),1).detach())), dim=1))/2.0 # variance = (torch.sum(self.kl_distance(self.log_sm(pred1),self.sm(pred2.detach())), dim=1) + \ # torch.sum(self.kl_distance(self.log_sm(pred1),self.sm(pred_ema.detach())), dim=1)) / 2.0 exp_variance = torch.exp(-variance) loss = torch.mean(loss_4layer * exp_variance) + torch.mean(loss_3layer* exp_variance) loss_reg = torch.mean(variance) return loss,loss_reg,exp_variance def update_variance_self(self, labels, pred1, tri_t, tri_t_ema): loss = self.criterion_ce(pred1, labels) variance = torch.sum(self.kl_distance(self.log_sm(tri_t),self.sm(tri_t_ema)), dim=1) exp_variance = torch.exp(-variance) loss = torch.mean(loss * exp_variance) + torch.mean(variance) return loss, exp_variance def softmax_kl_loss(self, input_logits, target_logits): """Takes softmax on both sides and returns KL divergence Note: - Returns the sum over all examples. Divide by the batch size afterwards if you want the mean. - Sends gradients to inputs but not the targets. """ assert input_logits.size() == target_logits.size() input_log_softmax = F.log_softmax(input_logits, dim=1) target_softmax = F.softmax(target_logits / 0.2, dim=1) return F.kl_div(input_log_softmax, target_softmax, size_average=False) def range_spbn(self, inputs_1_s, inputs_1_t): # arrange batch for domain-specific BN device_num = torch.cuda.device_count() B, C, H, W = inputs_1_s.size() def reshape(inputs): return inputs.view(device_num, -1, C, H, W) inputs_1_s, inputs_1_t = reshape(inputs_1_s), reshape(inputs_1_t) inputs = torch.cat((inputs_1_s, inputs_1_t), 1).view(-1, C, H, W) return inputs def derange_spbn(self, f_out): device_num = torch.cuda.device_count() # de-arrange batch f_out = f_out.view(device_num, -1, f_out.size(-1)) f_out_s, f_out_t = f_out.split(f_out.size(1) // 2, dim=1) f_out_s, f_out_t = f_out_s.contiguous().view(-1, f_out.size(-1)), f_out_t.contiguous().view(-1, f_out.size(-1)) return f_out_s, f_out_t def get_shuffle_ids(self, bsz): """generate shuffle ids for shufflebn""" forward_inds = torch.randperm(bsz).long().cuda() backward_inds = torch.zeros(bsz).long().cuda() value = torch.arange(bsz).long().cuda() backward_inds.index_copy_(0, forward_inds, value) return forward_inds, backward_inds def _update_ema_variables(self, model, ema_model, alpha, global_step): alpha = min(1 - 1 / (global_step + 1), alpha) for (ema_name, ema_param), (model_name, param) in zip(ema_model.named_parameters(), model.named_parameters()): ema_param.data.mul_(alpha).add_(1 - alpha, param.data) def _parse_data(self, inputs): # imgs_1, imgs_2, pids,...,pids2, index = inputs inputs_1 = inputs[0].cuda() inputs_2 = inputs[1].cuda() pids = [] for i, pid in enumerate(inputs[3:-2]): pids.append(pid.cuda()) index = inputs[-1].cuda() pids.append(pid.cuda()) return [inputs_1, inputs_2] + pids + [index] class DbscanBaseTrainer(object): def __init__(self, model_1, model_1_ema, contrast, num_cluster=None, alpha=0.999, fc_len=3000): super(DbscanBaseTrainer, self).__init__() self.model_1 = model_1 self.num_cluster = num_cluster self.c_name = [fc_len for _ in range(len(num_cluster))] self.model_1_ema = model_1_ema self.alpha = alpha self.criterion_ce = CrossEntropyLabelSmooth(self.num_cluster[0],False).cuda() self.criterion_tri = SoftTripletLoss_vallia(margin=0.0).cuda() self.source_classes = 751 self.contrast = contrast def train(self, epoch, data_loader_target, data_loader_source, optimizer, choice_c, print_freq=100, train_iters=200): self.model_1.train() self.model_1_ema.train() batch_time = AverageMeter() data_time = AverageMeter() losses_ce = [AverageMeter(), AverageMeter()] losses_tri = [AverageMeter(), AverageMeter()] loss_kldiv = AverageMeter() loss_s = AverageMeter() losses_tri_unc = AverageMeter() contra_loss = AverageMeter() precisions = [AverageMeter(), AverageMeter()] end = time.time() for i in range(train_iters): target_inputs = data_loader_target.next() source_inputs = data_loader_source.next() data_time.update(time.time() - end) # process inputs items = self._parse_data(target_inputs) items_source = self._parse_data(source_inputs) inputs_1_t, inputs_2_t, index_t = items[0], items[1], items[-1] inputs_1_s, inputs_2_s, index_s = items_source[0], items_source[1], items_source[-1] inputs = self.range_spbn(inputs_1_s, inputs_1_t) f_out, p_out, memory_f, _ = self.model_1(inputs, training=True) f_out_s1, f_out_t1 = self.derange_spbn(f_out) _, p_out_t1 = self.derange_spbn(p_out[0]) _, memory_f_t1 = self.derange_spbn(memory_f) with torch.no_grad(): f_out_ema, p_out_ema, memory_f_ema, _ = self.model_1_ema(inputs, training=True) f_out_s1_ema, f_out_t1_ema = self.derange_spbn(f_out_ema) _, p_out_t1_ema = self.derange_spbn(p_out_ema[0]) _, memory_f_t1_ema = self.derange_spbn(memory_f_ema) loss_tri_1 = self.criterion_tri(f_out_t1, f_out_t1, items[choice_c + 2]) loss_ce_1=self.criterion_ce(p_out_t1, items[2]) contra_loss_instance, contra_loss_center, ml_sour, ml_sour_ema = torch.tensor(0),torch.tensor(0),torch.tensor(0),torch.tensor(0) #self.contrast(memory_f_t1, f_out_s1, f_out_t1, f_out_t1_ema, index_t, items_source[2], epoch=epoch) loss_kl =loss_tri_unc= torch.tensor(0) loss = loss_ce_1 + loss_tri_1 # if epoch % 6 != 0: # loss = loss_ce_1 + loss_tri_1 + contra_loss_center + contra_loss_instance # else: # loss = loss_ce_1 + loss_tri_1 + contra_loss_center optimizer.zero_grad() loss.backward() optimizer.step() self._update_ema_variables(self.model_1, self.model_1_ema, self.alpha, epoch * len(data_loader_target) + i) prec_1, = accuracy(p_out_t1.data, items[choice_c + 2].data) losses_ce[0].update(loss_ce_1.item()) losses_tri[0].update(loss_tri_1.item()) loss_s.update(contra_loss_center.item()) loss_kldiv.update(loss_kl.item()) losses_tri_unc.update(loss_tri_unc.item()) contra_loss.update(contra_loss_instance.item()) precisions[0].update(prec_1[0]) # print log # batch_time.update(time.time() - end) end = time.time() if (i + 1) % print_freq == 1: print('Epoch: [{}][{}/{}]\t' 'Time {:.3f} ({:.3f})\t' 'Data {:.3f} ({:.3f})\t' 'Loss_ce {:.3f} / loss_kldiv {:.3f}\t' 'Loss_tri {:.3f} / Loss_tri_soft {:.3f} \t' 'contra_loss_center {:.3f}\t' 'contra_loss {:.3f}\t' 'Prec {:.2%} / {:.2%}\t' .format(epoch, i, len(data_loader_target), batch_time.val, batch_time.avg, data_time.val, data_time.avg, losses_ce[0].avg, loss_kldiv.avg, losses_tri[0].avg,losses_tri_unc.avg, loss_s.avg, contra_loss.avg, precisions[0].avg, precisions[1].avg)) def range_spbn(self, inputs_1_s, inputs_1_t): # arrange batch for domain-specific BN device_num = torch.cuda.device_count() B, C, H, W = inputs_1_s.size() def reshape(inputs): return inputs.view(device_num, -1, C, H, W) inputs_1_s, inputs_1_t = reshape(inputs_1_s), reshape(inputs_1_t) inputs = torch.cat((inputs_1_s, inputs_1_t), 1).view(-1, C, H, W) return inputs def derange_spbn(self, f_out): device_num = torch.cuda.device_count() # de-arrange batch f_out = f_out.view(device_num, -1, f_out.size(-1)) f_out_s, f_out_t = f_out.split(f_out.size(1) // 2, dim=1) f_out_s, f_out_t = f_out_s.contiguous().view(-1, f_out.size(-1)), f_out_t.contiguous().view(-1, f_out.size(-1)) return f_out_s, f_out_t def _update_ema_variables(self, model, ema_model, alpha, global_step): alpha = min(1 - 1 / (global_step + 1), alpha) for (ema_name, ema_param), (model_name, param) in zip(ema_model.named_parameters(), model.named_parameters()): ema_param.data.mul_(alpha).add_(1 - alpha, param.data) def _parse_data(self, inputs): # imgs_1, imgs_2, pids,...,pids2, index = inputs inputs_1 = inputs[0].cuda() inputs_2 = inputs[1].cuda() pids = [] for i, pid in enumerate(inputs[3:-2]): pids.append(pid.cuda()) index = inputs[-1].cuda() pids.append(pid.cuda()) return [inputs_1, inputs_2] + pids + [index]

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/dist_metric.py

from __future__ import absolute_import import torch from .evaluators import extract_features from .metric_learning import get_metric class DistanceMetric(object): def __init__(self, algorithm='euclidean', *args, **kwargs): super(DistanceMetric, self).__init__() self.algorithm = algorithm self.metric = get_metric(algorithm, *args, **kwargs) def train(self, model, data_loader): if self.algorithm == 'euclidean': return features, labels = extract_features(model, data_loader) features = torch.stack(features.values()).numpy() labels = torch.Tensor(list(labels.values())).numpy() self.metric.fit(features, labels) def transform(self, X): if torch.is_tensor(X): X = X.numpy() X = self.metric.transform(X) X = torch.from_numpy(X) else: X = self.metric.transform(X) return X

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/multigpu.py

import time import torch # from util import MovingAverage def aggreg_multi_gpu(model, dataloader, hc, dim, TYPE=torch.float64, model_gpus=1): """"Accumulate activations and save them on multiple GPUs * this function assumes the model is on the first `model_gpus` GPUs so that it can write the activations on the remaining ones * it splits the activations evenly between the remaining GPUs """ # number of gpus to store ngpu_store = torch.cuda.device_count() - model_gpus#3-1 # number of batches in DL l_dl = len(dataloader)#50000 # number of batches each gpu gets batches_per_gpu = l_dl // ngpu_store#16666 # number of data each gpu gets points_per_gpu = batches_per_gpu*dataloader.batch_size # empty array of indices that we need to keep track of indices = torch.empty(len(dataloader.dataset), dtype=torch.long) # set up matrix PS: (N x K) when using one head, otherwise N x D, where D is the dim before the last FC layer. PS = [torch.empty(points_per_gpu, dim, device='cuda:' + str(i), dtype=TYPE) for i in range(model_gpus, model_gpus + ngpu_store-1)] # accomodate remainder PS.append(torch.empty(len(dataloader.dataset) - (ngpu_store-1)*points_per_gpu, dim, device='cuda:' + str(model_gpus + ngpu_store - 1), dtype=TYPE))#把除不尽的sample包括进去 # slice sizes, i.e. how many activations will be on the gpus slices = [qq.shape[0] for qq in PS] print("slice sizes: ", slices, flush=True) # batch_time = MovingAverage(intertia=0.9) now = time.time() st = 0 softmax = torch.nn.Softmax(dim=1).to('cuda:0') # switch the model to not output array but instead last-FC output for one head and pre-last activations for multi-heads model.headcount = 1 for batch_idx, (data, _, _,_,_selected) in enumerate(dataloader): data = data.to(torch.device('cuda:0')) mass = data.size(0) en = st + mass # j keeps track of which part of PS we're writing to j = min((batch_idx // batches_per_gpu), ngpu_store - 1) subs = j*points_per_gpu if hc == 1: _,predicted_,_=model(data) p = softmax(predicted_).detach().to(TYPE) # when using one head: save softmax (N x K) matrix: PS[j][st-subs:en-subs, :].copy_(p) else: # when using multiple heads: save softmax (N x D) matrix PS[j][st-subs:en-subs, :].copy_(model(data).detach()) indices[st:en].copy_(_selected) st = en # batch_time.update(time.time() - now) now = time.time() if batch_idx % 50 == 0: print(f"Aggregating batch {batch_idx:03}/{l_dl}, speed: {mass / batch_time.avg:04.1f}Hz. To rGPU {j+1}", end='\r', flush=True) torch.cuda.synchronize() # just in case return PS, indices def gpu_mul_Ax(A, b, ngpu, splits, TYPE=torch.float64,model_gpus=1): """ multiplies matrix A (stored on multiple GPUs) with vector x * returns vector on GPU 0 """ # Step 1: make a copy of B on each GPU N = splits[-1] b_ = [] for i in range(model_gpus, ngpu): b_.append(b.to('cuda:' + str(i))) # Step 2: issue the matmul on each GPU c = torch.empty(N, 1, device='cuda:0', dtype=TYPE) for a,i in enumerate(range(model_gpus, ngpu)): c[splits[a]:splits[a+1], :].copy_(torch.matmul(A[a], b_[a])) return c def gpu_mul_AB(A, B, c, dim, TYPE=torch.float64, model_gpus=1): """" multiplies to matrices A,B on GPU and adds vector c and does softmax at the end * used to compute the effect of a linear FC layer followed by softmax * return (N x K) matrix spread over the same GPUs as the PS matrix """ # Step 1: make a copy of B on each GPU ngpu = torch.cuda.device_count() # one for the model b_ = [] for i in range(model_gpus, ngpu): b_.append(B.to('cuda:' + str(i))) # Step 2: issue the matmul on each GPU PS = [] for a, i in enumerate(range(model_gpus, ngpu)): PS.append((torch.matmul(A[a], b_[a]) + c.to('cuda:'+str(i))).to(TYPE)) # the softmax torch.exp(PS[a], out=PS[a]) summed = torch.sum(PS[a], dim=1, keepdim=True) PS[a] /= summed return PS def gpu_mul_xA(b, A, ngpu, splits, TYPE=torch.float64, model_gpus=1): """ multiplies vector x with matrix A (stored on multiple GPUs) * returns vector on GPU 0 """ # Step 1: make a copy of B on each GPU b_ = [] for a, i in enumerate(range(model_gpus, ngpu)): b_.append(b[:, splits[a]:splits[a+1]].to('cuda:' + str(i))) # Step 2: issue the matmul on each GPU c = torch.empty(ngpu-model_gpus, A[0].size(1), device='cuda:0', dtype=TYPE) for a, i in enumerate(range(model_gpus, ngpu)): c[a:a+1, :].copy_(torch.matmul(b_[a], A[a])) # Step 3: need to sum these up torch.cuda.synchronize() # just in case c = torch.sum(c, 0, keepdim=True) return c

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/sinkhornknopp.py

import torch import torch.nn as nn import time import numpy as np from UDAsbs.multigpu import gpu_mul_Ax, gpu_mul_xA, aggreg_multi_gpu, gpu_mul_AB from scipy.special import logsumexp def py_softmax(x, axis=None): """stable softmax""" return np.exp(x - logsumexp(x, axis=axis, keepdims=True)) def cpu_sk(self): """ Sinkhorn Knopp optimization on CPU * stores activations to RAM * does matrix-vector multiplies on CPU * slower than GPU """ # 1. aggregate inputs: self.model.eval() N = len(self.pseudo_loader.dataset) if self.hc == 1: self.PS = np.zeros((N, self.K[0]), dtype=self.dtype) else: self.PS_pre = np.zeros((N, self.presize), dtype=self.dtype) now = time.time() for batch_idx, item in enumerate(self.pseudo_loader): data = item[0].to(self.dev) if self.hc == 1: _,predicted,_=self.model(data,training=True)# predicted=self.model(data,cluster=True) p = nn.functional.softmax(predicted[0], 1) self.PS[item[-1], :] = p.detach().cpu().numpy().astype(self.dtype) else: self.model.headcount = self.hc p = self.model(data) self.PS_pre[item[-1], :] = p.detach().cpu().numpy().astype(self.dtype) print("Aggreg of outputs took {0:.2f} min".format((time.time() - now) / 60.), flush=True) # 2. solve label assignment via sinkhorn-knopp: if self.hc == 1: optimize_L_sk(self, nh=0) else: for nh in range(self.hc): print("computing head %s " % nh, end="\r", flush=True) tl = getattr(self.model.module, "classifier{}_{}".format(nh,self.K[nh])) time_mat = time.time() # clear memory try: del self.PS except: pass # apply last FC layer (a matmul and adding of bias) self.PS = self.PS_pre @ tl.weight.cpu().detach().numpy().T.astype(self.dtype) # + tl.bias.cpu().detach().numpy().astype(self.dtype)) print("matmul took %smin" % ((time.time() - time_mat) / 60.), flush=True) self.PS = py_softmax(self.PS, 1) optimize_L_sk(self, nh=nh) return def gpu_sk(self): """ Sinkhorn Knopp optimization on GPU * stores activations on multiple GPUs (needed when dataset is large) * does matrix-vector multiplies on GPU (extremely fast) * recommended variant * due to multi-GPU use, it's a bit harder to understand what's happening -> see CPU variant to understand """ # 1. aggregate inputs: start_t = time.time() if self.hc == 1: self.PS, indices = aggreg_multi_gpu(self.model, self.pseudo_loader, hc=self.hc, dim=self.outs[0], TYPE=self.dtype) else: try: # just in case stuff del self.PS_pre except: pass torch.cuda.empty_cache() time.sleep(1) self.PS_pre, indices = aggreg_multi_gpu(self.model, self.pseudo_loader, hc=self.hc, dim=self.presize, TYPE=torch.float32) self.model.headcount = self.hc print("Aggreg of outputs took {0:.2f} min".format((time.time() - start_t) / 60.), flush=True) # 2. solve label assignment via sinkhorn-knopp: if self.hc == 1: optimize_L_sk_multi(self, nh=0) self.L[0,indices] = self.L[0,:] else: for nh in range(self.hc): tl = getattr(self.model, "top_layer%d" % nh) time_mat = time.time() try: del self.PS torch.cuda.empty_cache() except: pass # apply last FC layer (a matmul and adding of bias) self.PS = gpu_mul_AB(self.PS_pre, tl.weight.t(), c=tl.bias, dim=self.outs[nh], TYPE=self.dtype) print("matmul took %smin" % ((time.time() - time_mat) / 60.), flush=True) optimize_L_sk_multi(self, nh=nh) self.L[nh][indices] = self.L[nh] return import collections def optimize_L_sk(self, nh=0): N = max(self.L[nh].size()) tt = time.time() self.PS = self.PS.T # now it is K x N if not self.dis_gt: r = np.ones((self.outs[nh], 1), dtype=self.dtype) / self.outs[nh] else: b_pesud_label = np.nanargmax(self.PS, 0) plabel2number=dict(collections.Counter(b_pesud_label)).items() plabel2number=sorted(plabel2number,key=lambda plabel2number:plabel2number[1]) sort_label=[label[0] for label in plabel2number] origin_dis=self.dis_gt deta=len(origin_dis)/ self.outs[nh] r = np.ones((self.outs[nh], 1), dtype=self.dtype) / N for i,sl in enumerate(sort_label[::-1]): nn=origin_dis[0 + int(round(i * deta))] r[sl,:] = nn r=py_softmax(r,axis=0) c = np.ones((N, 1), dtype=self.dtype) / N self.PS **= self.lamb # K x N inv_K = self.dtype(1./self.outs[nh]) inv_N = self.dtype(1./N) err = 1e6 _counter = 0 while err > 1e-2: r = inv_K / (self.PS @ c) # (KxN)@(N,1) = K x 1 c_new = inv_N / (r.T @ self.PS).T # ((1,K)@(KxN)).t() = N x 1 if _counter % 10 == 0: err = np.nansum(np.abs(c / c_new - 1)) c = c_new _counter += 1 print("error: ", err, 'step ', _counter, flush=True) # " nonneg: ", sum(I), flush=True) # inplace calculations. self.PS *= np.squeeze(c) self.PS = self.PS.T self.PS *= np.squeeze(r) self.PS = self.PS.T argmaxes = np.nanargmax(self.PS, 0) # size N newL = torch.LongTensor(argmaxes) self.L[nh] = newL.to(self.dev) print('opt took {0:.2f}min, {1:4d}iters'.format(((time.time() - tt) / 60.), _counter), flush=True) def optimize_L_sk_multi(self, nh=0): """ optimizes label assignment via Sinkhorn-Knopp. this implementation uses multiple GPUs to store the activations which allow fast matrix multiplies Parameters: nh (int) number of the head that is being optimized. """ N = max(self.L.size()) tt = time.time() r = torch.ones((self.outs[nh], 1), device='cuda:0', dtype=self.dtype) / self.outs[nh] c = torch.ones((N, 1), device='cuda:0', dtype=self.dtype) / N ones = torch.ones(N, device='cuda:0', dtype=self.dtype) inv_K = 1. / self.outs[nh] inv_N = 1. / N # inplace power of softmax activations: [qq.pow_(self.lamb) for qq in self.PS] # K x N err = 1e6 _counter = 0 ngpu = torch.cuda.device_count() splits = np.cumsum([0] + [a.size(0) for a in self.PS]) while err > 1e-1: r = inv_K / (gpu_mul_xA(c.t(), self.PS, ngpu=ngpu, splits=splits, TYPE=self.dtype)).t() # ((1xN)@(NxK)).T = Kx1 c_new = inv_N / (gpu_mul_Ax(self.PS, r, ngpu=ngpu, splits=splits, TYPE=self.dtype)) # (NxK)@(K,1) = N x 1 torch.cuda.synchronize() # just in case if _counter % 10 == 0: err = torch.sum(torch.abs((c.squeeze() / c_new.squeeze()) - ones)).cpu().item() c = c_new _counter += 1 print("error: ", err, 'step ', _counter, flush=True) # getting the final tranportation matrix ##################### for i, qq in enumerate(self.PS): torch.mul(qq, c[splits[i]:splits[i + 1], :].to('cuda:' + str(i + 1)), out=qq) [torch.mul(r.to('cuda:' + str(i + 1)).t(), qq, out=qq) for i, qq in enumerate(self.PS)] argmaxes = torch.empty(N, dtype=torch.int64, device='cuda:0') start_idx = 0 for i, qq in enumerate(self.PS): amax = torch.argmax(qq, 1) argmaxes[start_idx:start_idx + len(qq)].copy_(amax) start_idx += len(qq) newL = argmaxes print('opt took {0:.2f}min, {1:4d}iters'.format(((time.time() - tt) / 60.), _counter), flush=True) # finally, assign the new labels ######################## self.L[nh] = newL

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/memorybank/alias_multinomial.py

import torch class AliasMethod(object): """ From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/ """ def __init__(self, probs): if probs.sum() > 1: probs.div_(probs.sum()) K = len(probs) self.prob = torch.zeros(K) self.alias = torch.LongTensor([0]*K) # Sort the data into the outcomes with probabilities # that are larger and smaller than 1/K. smaller = [] larger = [] for kk, prob in enumerate(probs): self.prob[kk] = K*prob if self.prob[kk] < 1.0: smaller.append(kk) else: larger.append(kk) # Loop though and create little binary mixtures that # appropriately allocate the larger outcomes over the # overall uniform mixture. while len(smaller) > 0 and len(larger) > 0: small = smaller.pop() large = larger.pop() self.alias[small] = large self.prob[large] = (self.prob[large] - 1.0) + self.prob[small] if self.prob[large] < 1.0: smaller.append(large) else: larger.append(large) for last_one in smaller+larger: self.prob[last_one] = 1 def cuda(self): self.prob = self.prob.cuda() self.alias = self.alias.cuda() def draw(self, N): """ Draw N samples from multinomial :param N: number of samples :return: samples """ K = self.alias.size(0) kk = torch.zeros(N, dtype=torch.long, device=self.prob.device).random_(0, K) prob = self.prob.index_select(0, kk) alias = self.alias.index_select(0, kk) # b is whether a random number is greater than q b = torch.bernoulli(prob) oq = kk.mul(b.long()) oj = alias.mul((1-b).long()) return oq + oj

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/memorybank/NCEAverage.py

import torch from torch import nn from torch.nn import functional as F import math from numpy.testing import assert_almost_equal def normalize(x, axis=-1): """Normalizing to unit length along the specified dimension. Args: x: pytorch Variable Returns: x: pytorch Variable, same shape as input """ x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12) return x def sigmoid(tensor, temp=1.0): exponent = -tensor / temp exponent = torch.clamp(exponent, min=-50, max=50) y = 1.0 / (1.0 + torch.exp(exponent)) return y def logsumexp(value, weight=1, dim=None, keepdim=False): """Numerically stable implementation of the operation value.exp().sum(dim, keepdim).log() """ # TODO: torch.max(value, dim=None) threw an error at time of writing if dim is not None: m, _ = torch.max(value, dim=dim, keepdim=True) value0 = value - m if keepdim is False: m = m.squeeze(dim) return m + torch.log(torch.sum(weight*torch.exp(value0), dim=dim, keepdim=keepdim)) else: m = torch.max(value) sum_exp = torch.sum(weight*torch.exp(value - m)) return m + torch.log(sum_exp) class MemoryMoCo_id(nn.Module): """Fixed-size queue with momentum encoder""" def __init__(self, inputSize, outputSize, K, index2label, choice_c=1, T=0.07, use_softmax=False, cluster_num=0): super(MemoryMoCo_id, self).__init__() self.outputSize = outputSize self.inputSize = inputSize self.queueSize = K self.T = T self.index = 0 self.use_softmax = use_softmax self.register_buffer('params', torch.tensor([-1])) stdv = 1. / math.sqrt(inputSize / 3) self.register_buffer('memory', torch.rand(self.queueSize, inputSize).mul_(2 * stdv).add_(-stdv)) self.register_buffer('index_memory', torch.ones(self.queueSize, dtype=torch.long).fill_(-1)) print('Using queue shape: ({},{})'.format(self.queueSize, inputSize)) self.choice_c=choice_c self.index_pl = -1#3-cluster_num if cluster_num<=3 else 0 self.index2label = index2label self.m = 0.25 self.gamma = 128 def posandneg(self, index, batchSize, index_choice): # pseudo logit # pseudo_label = [torch.tensor([self.index2label[j][i.item()] for i in index], dtype=torch.long).cuda() # for j in range(4)] # pseudo_label = sum(pseudo_label) / 4.0 # pseudo_label=reduce(lambda x, y: x * y, pseudo_label) pseudo_label = torch.tensor([self.index2label[index_choice][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.queueSize) # memory_label = [ # torch.tensor([self.index2label[j][i.item()] if i.item() != -1 else -1 for i in self.index_memory], # dtype=torch.long).cuda() # for j in range(4)] # memory_label = sum(memory_label) / 4.0 # memory_label = reduce(lambda x, y: x * y, memory_label) memory_label = torch.tensor([self.index2label[index_choice][i.item()] if i.item() != -1 else -1 for i in self.index_memory], dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.queueSize) is_pos = pseudo_label.eq(memory_label).float() # is_pos_weight = torch.cat((torch.ones([batchSize, 1], dtype=torch.float).cuda(), is_pos), dim=1) # weight = torch.cat( # (torch.ones([batchSize, 1], dtype=torch.float).cuda(), is_pos / is_pos_weight.sum(1, keepdim=True)), dim=1) # is_pos = is_pos_weight is_pos = torch.cat((torch.ones([batchSize, 1], dtype=torch.float).cuda(), is_pos), dim=1) is_neg = pseudo_label.ne(memory_label).float() is_neg = torch.cat((torch.zeros([batchSize, 1], dtype=torch.float).cuda(), is_neg), dim=1) # is_neg = torch.cat((torch.zeros([batchSize, 1], dtype=torch.float).cuda(), is_neg), dim=1) return is_pos, is_neg def update(self,q1, q2, index): batchSize = q1.shape[0] with torch.no_grad(): q1 = q1.detach() q2 = q2.detach() out_ids = torch.arange(batchSize).cuda() out_ids += self.index out_ids = torch.fmod(out_ids, self.queueSize) out_ids = out_ids.long() self.memory.index_copy_(0, out_ids, (q1+q2)/2.0) self.index_memory.index_copy_(0, out_ids, index) self.index = (self.index + batchSize) % self.queueSize def forward(self, q1, q2, index, epoch=0): batchSize = q1.shape[0] q1 = normalize(q1, axis=-1) q2 = normalize(q2, axis=-1) #is_pos0, is_neg0 = self.posandneg(index, batchSize, 0) is_pos1, is_neg1 = self.posandneg(index, batchSize, self.choice_c) #is_pos2, is_neg2 = self.posandneg(index, batchSize, 2) #is_pos3, is_neg3 = self.posandneg(index, batchSize, 3) is_pos =is_pos1# (is_pos0 + is_pos1 + is_pos2 + is_pos3)/4.0 is_neg =is_neg1# (is_neg0 + is_neg1 + is_neg2 + is_neg3)/4.0 queue = self.memory.clone() l_logist = torch.matmul(queue.detach(), ((q1+q2)/2.0).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() # (bs, queue_size) # pos logit for self l_pos = torch.bmm(q1.view(batchSize, 1, -1), q2.view(batchSize, -1, 1)) l_pos_self = l_pos.contiguous().view(batchSize, 1) sim_mat = torch.cat((l_pos_self, l_logist), dim=1) s_p = sim_mat * is_pos#0#[is_pos].contiguous()#.view(batchSize, -1) # s_p = torch.div(s_p, self.T) s_n = sim_mat * is_neg#3#[is_neg].contiguous()#.view(batchSize, -1) alpha_p = F.relu(-s_p.detach() + 1 + self.m) alpha_n = F.relu(s_n.detach() + self.m) delta_p = 1 - self.m delta_n = self.m logit_p = - self.gamma * alpha_p * (s_p - delta_p) logit_n = self.gamma * alpha_n * (s_n - delta_n) # logit_p_1 = torch.exp(logit_p * is_pos) * is_pos # logit_p_2 = logit_p_1.sum(1) # logit_p_3 = torch.log(logit_p_2+ 1e-16) # logit_n = torch.log((torch.exp(logit_n) * is_neg).sum(1) + 1e-16) # loss = F.softplus(logit_p+logit_n).mean() loss = F.softplus(logsumexp(logit_p - 99999.0 * is_neg, dim=1) + logsumexp(logit_n - 99999.0 * is_pos, dim=1)).mean() / 18.0 # weight, #loss= F.softplus(logsumexp(logit_p - 99999.0 * is_neg0,is_pos, dim=1) + # logsumexp(logit_n - 99999.0 * is_pos3,is_neg, dim=1)).mean()/18.0#weight, # update memory with torch.no_grad(): q1 = q1.detach() q2 = q2.detach() out_ids = torch.arange(batchSize).cuda() out_ids += self.index out_ids = torch.fmod(out_ids, self.queueSize) out_ids = out_ids.long() self.memory.index_copy_(0, out_ids, (q1+q2)/2.0) self.index_memory.index_copy_(0, out_ids, index) self.index = (self.index + batchSize) % self.queueSize return loss class onlinememory(nn.Module): """Fixed-size queue with momentum encoder""" def __init__(self, inputSize, outputSize, sour_numclass, K, index2label, choice_c=1, T=0.07, use_softmax=False, cluster_num=0): super(onlinememory, self).__init__() self.outputSize = outputSize self.inputSize = inputSize self.sour_numclass = sour_numclass self.queueSize = K self.T = T self.index = 0 self.use_softmax = use_softmax self.register_buffer('params', torch.tensor([-1])) stdv = 1. / math.sqrt(inputSize / 3) self.register_buffer('memory', torch.rand(self.queueSize, inputSize).mul_(2 * stdv).add_(-stdv)) self.register_buffer('index_memory', torch.ones(self.queueSize, dtype=torch.long).fill_(-1)) self.register_buffer('uncer', torch.ones(self.queueSize, dtype=torch.float).fill_(1)) print('Using queue shape: ({},{})'.format(self.queueSize, inputSize)) # self.register_buffer('sour_memory', torch.rand(self.sour_numclass, inputSize).mul_(2 * stdv).add_(-stdv)) # self.register_buffer('sour_index_memory', torch.ones(self.sour_numclass, dtype=torch.long).fill_(-1)) # print('Using queue shape: ({},{})'.format(self.sour_numclass, inputSize)) self.choice_c = choice_c self.index_pl = -1 # 3-cluster_num if cluster_num<=3 else 0 self.index2label = index2label self.m = 0.25 self.gamma = 128 self.momentum=0.2 ################ #his loss num_steps=151 self.step = 2 / (num_steps - 1) self.eps = 1 / num_steps self.t = torch.arange(-1, 1 + self.step, self.step).view(-1, 1).cuda() self.tsize = self.t.size()[0] ############### # smooth ap loss self.anneal = 0.01 self.num_id=16 def memo_contr_loss(self,index,q1): batchSize = q1.shape[0] # import ipdb;ipdb.set_trace() pseudo_label = torch.tensor([self.index2label[self.choice_c][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.queueSize) memory_label = torch.tensor( [self.index2label[self.choice_c][i.item()] if i.item() != -1 else -1 for i in self.index_memory], dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.queueSize) is_pos = pseudo_label.eq(memory_label).float() is_neg = pseudo_label.ne(memory_label).float() queue = self.memory.clone() l_logist = torch.matmul(queue.detach(), (q1).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() sim_mat = l_logist outputs = F.log_softmax(sim_mat, dim=1) loss = - (is_pos * outputs) loss = loss.sum(dim=1) loss = loss.mean(dim=0) return loss def memo_circle_loss(self,index,q1,uncer): batchSize = q1.shape[0] # import ipdb;ipdb.set_trace() pseudo_label = torch.tensor([self.index2label[self.choice_c][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.queueSize) memory_label = torch.tensor( [self.index2label[self.choice_c][i.item()] if i.item() != -1 else -1 for i in self.index_memory], dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.queueSize) is_pos = pseudo_label.eq(memory_label).float() is_neg = pseudo_label.ne(memory_label).float() queue = self.memory.clone() l_logist = torch.matmul(queue.detach(), (q1).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() sim_mat = l_logist # exp_variance = exp_variance.detach() # exp_variance = exp_variance.unsqueeze(1).expand(batchSize, self.queueSize) s_p = sim_mat * is_pos #s_p = torch.div(s_p, self.T) s_n = sim_mat * is_neg #* exp_variance exp_variance = 1#(uncer.unsqueeze(1).expand(batchSize, self.queueSize) + self.uncer.clone().unsqueeze(0).expand(batchSize, self.queueSize))/2.0 alpha_p = F.relu(-s_p.detach() + 1 + self.m) alpha_n = F.relu(s_n.detach() + self.m) delta_p = 1 - self.m delta_n = self.m logit_p = - self.gamma * alpha_p * (s_p - delta_p) logit_n = self.gamma * alpha_n * (s_n - delta_n) # ,weight=exp_variance loss = (F.softplus(logsumexp(logit_p - 99999.0 * is_neg,weight=exp_variance, dim=1) + logsumexp(logit_n - 99999.0 * is_pos,weight=exp_variance, dim=1))).mean() return loss def memo_center_circle_loss(self,index,q1): batchSize = q1.shape[0] pseudo_label = torch.tensor([self.index2label[self.choice_c][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.sour_numclass) # pseudo_label = index.expand(batchSize, self.sour_numclass) memory_label = torch.tensor( [self.index2label[self.choice_c][i] for i in range(self.sour_numclass)], dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.sour_numclass) is_pos = pseudo_label.eq(memory_label).float() is_neg = pseudo_label.ne(memory_label).float() queue = self.memory[:self.sour_numclass,:].clone() l_logist = torch.matmul(queue.detach(), (q1).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() sim_mat = l_logist s_p = sim_mat * is_pos s_n = sim_mat * is_neg alpha_p = F.relu(-s_p.detach() + 1 + self.m) alpha_n = F.relu(s_n.detach() + self.m) delta_p = 1 - self.m delta_n = self.m logit_p = - self.gamma * alpha_p * (s_p - delta_p) logit_n = self.gamma * alpha_n * (s_n - delta_n) loss = F.softplus(logsumexp(logit_p - 99999.0 * is_neg, dim=1) + logsumexp(logit_n - 99999.0 * is_pos, dim=1)).mean() / 18.0 return loss def his_loss(self,classes,features): classes = torch.tensor([self.index2label[0][i.item()] for i in classes], dtype=torch.long).cuda() def histogram(inds, size): s_repeat_ = s_repeat.clone() indsa = (s_repeat_floor - (self.t - self.step) > -self.eps) & ( s_repeat_floor - (self.t - self.step) < self.eps) & inds assert indsa.nonzero().size()[0] == size, ('Another number of bins should be used') zeros = torch.zeros((1, indsa.size()[1])).byte() if self.cuda: zeros = zeros.cuda() indsb = torch.cat((indsa, zeros))[1:, :] s_repeat_[~(indsb | indsa)] = 0 # indsa corresponds to the first condition of the second equation of the paper s_repeat_[indsa] = (s_repeat_ - self.t + self.step)[indsa] / self.step # indsb corresponds to the second condition of the second equation of the paper s_repeat_[indsb] = (-s_repeat_ + self.t + self.step)[indsb] / self.step return s_repeat_.sum(1) / size classes_size = classes.size()[0] classes_eq = (classes.repeat(classes_size, 1) == classes.view(-1, 1).repeat(1, classes_size)).data dists = torch.mm(features, features.transpose(0, 1)) assert ((dists > 1 + self.eps).sum().item() + ( dists < -1 - self.eps).sum().item()) == 0, 'L2 normalization should be used' s_inds = torch.triu(torch.ones(classes_eq.size()), 1).byte() if self.cuda: s_inds = s_inds.cuda() pos_inds = classes_eq[s_inds].repeat(self.tsize, 1)#18001,2016 neg_inds = ~classes_eq[s_inds].repeat(self.tsize, 1)#18001,2016 pos_size = classes_eq[s_inds].sum().item() neg_size = (~classes_eq[s_inds]).sum().item() s = dists[s_inds].view(1, -1) s_repeat = s.repeat(self.tsize, 1) s_repeat_floor = (torch.floor(s_repeat.data / self.step) * self.step).float() histogram_pos = histogram(pos_inds, pos_size) assert_almost_equal(histogram_pos.sum().item(), 1, decimal=1, err_msg='Not good positive histogram', verbose=True) histogram_neg = histogram(neg_inds, neg_size) assert_almost_equal(histogram_neg.sum().item(), 1, decimal=1, err_msg='Not good negative histogram', verbose=True) histogram_pos_repeat = histogram_pos.view(-1, 1).repeat(1, histogram_pos.size()[0]) histogram_pos_inds = torch.tril(torch.ones(histogram_pos_repeat.size()), -1).byte() if self.cuda: histogram_pos_inds = histogram_pos_inds.cuda() histogram_pos_repeat[histogram_pos_inds] = 0 histogram_pos_cdf = histogram_pos_repeat.sum(0) loss = torch.sum(histogram_neg * histogram_pos_cdf) return loss def smooth_ap(self, targets,embedding): targets= torch.tensor([self.index2label[0][i.item()] for i in targets], dtype=torch.long).cuda() # For distributed training, gather all features from different process. all_embedding = self.memory.clone().detach() all_targets = torch.tensor( [self.index2label[0][i.item()] if i.item() != -1 else -1 for i in self.index_memory], dtype=torch.long).cuda() sim_dist = torch.matmul(embedding, all_embedding.t()) N, M = sim_dist.size() # Compute the mask which ignores the relevance score of the query to itself mask_indx = 1.0 - torch.eye(M, device=sim_dist.device) mask_indx = mask_indx.unsqueeze(dim=0).repeat(N, 1, 1) # (N, M, M) # sim_dist -> N, 1, M -> N, M, N sim_dist_repeat = sim_dist.unsqueeze(dim=1).repeat(1, M, 1) # (N, M, M) # sim_dist_repeat_t = sim_dist.t().unsqueeze(dim=1).repeat(1, N, 1) # (N, N, M) # Compute the difference matrix sim_diff = sim_dist_repeat - sim_dist_repeat.permute(0, 2, 1) # (N, M, M) # Pass through the sigmoid sim_sg = sigmoid(sim_diff, temp=self.anneal) * mask_indx # Compute all the rankings sim_all_rk = torch.sum(sim_sg, dim=-1) + 1 # (N, N) pos_mask = targets.view(N, 1).expand(N, M).eq(all_targets.view(M, 1).expand(M, N).t()).float() # (N, M) pos_mask_repeat = pos_mask.unsqueeze(1).repeat(1, M, 1) # (N, M, M) # Compute positive rankings pos_sim_sg = sim_sg * pos_mask_repeat sim_pos_rk = torch.sum(pos_sim_sg, dim=-1) + 1 # (N, N) # sum the values of the Smooth-AP for all instances in the mini-batch ap = 0 group = N // self.num_id for ind in range(self.num_id): pos_divide = torch.sum( sim_pos_rk[(ind * group):((ind + 1) * group), (ind * group):((ind + 1) * group)] / ( sim_all_rk[(ind * group):((ind + 1) * group), (ind * group):((ind + 1) * group)])) ap += pos_divide / torch.sum(pos_mask[ind * group]) / N return 1 - ap def _smooth_ap(self, targets,embedding): """Forward pass for all input predictions: preds - (batch_size x feat_dims) """ # ------ differentiable ranking of all retrieval set ------ embedding = F.normalize(embedding, dim=1) # For distributed training, gather all features from different process. sim_dist = torch.matmul(embedding, self.memory[:self.queueSize-self.sour_numclass,:].t().detach()) N, M = sim_dist.size() # Compute the mask which ignores the relevance score of the query to itself mask_indx = 1.0 - torch.eye(M, device=sim_dist.device) mask_indx = mask_indx.unsqueeze(dim=0).repeat(N, 1, 1) # (N, M, M) # sim_dist -> N, 1, M -> N, M, N sim_dist_repeat = sim_dist.unsqueeze(dim=1).repeat(1, M, 1) # (N, M, M) # Compute the difference matrix sim_diff = sim_dist_repeat - sim_dist_repeat.permute(0, 2, 1) # (N, M, M) # Pass through the sigmoid sim_sg = sigmoid(sim_diff, temp=self.anneal) * mask_indx # Compute all the rankings sim_all_rk = torch.sum(sim_sg, dim=-1) + 1 # (N, N)r targets = torch.tensor([self.index2label[0][i.item()] for i in targets], dtype=torch.long).cuda() queue_label = torch.tensor([self.index2label[0][i.item()] if i.item() != -1 else -1 for i in self.index_memory], dtype=torch.long).cuda()[self.sour_numclass:] pos_mask = targets.view(N, 1).expand(N, M).eq(queue_label.view(M, 1).expand(M, N).t()).float() # (N, M) pos_mask_repeat = pos_mask.unsqueeze(1).repeat(1, M, 1) # (N, M, M) # Compute positive rankings pos_sim_sg = sim_sg * pos_mask_repeat sim_pos_rk = torch.sum(pos_sim_sg, dim=-1) + 1 # (N, N) # sum the values of the Smooth-AP for all instances in the mini-batch ap = 0 group = N // self.num_id for ind in range(self.num_id): pos_divide = torch.sum( sim_pos_rk[(ind * group):((ind + 1) * group), (ind * group):((ind + 1) * group)] / ( sim_all_rk[(ind * group):((ind + 1) * group), (ind * group):((ind + 1) * group)])) ap += pos_divide / torch.sum(pos_mask[ind * group]) / N return 1 - ap def forward(self, q1, q2, tar_tri, tar_tri_ema, index, sour_labels, uncer=None, epoch=0): batchSize = q1.shape[0] # tar_tri = normalize(tar_tri, axis=-1) q1 = normalize(q1, axis=-1) q2 = normalize(q2, axis=-1) # loss_q1 = self.memo_contr_loss(index+self.sour_numclass, q1) loss_q1 = self.memo_circle_loss(index + self.sour_numclass, q1, uncer) # loss_q1 = self._smooth_ap(index + self.sour_numclass, q1) loss_q2 = self.memo_center_circle_loss(sour_labels, q2) # with torch.no_grad(): # queue = self.memory[:self.sour_numclass, :].clone() # ml_sour = torch.matmul(tar_tri,queue.transpose(1, 0).detach()) # ml_sour_ema = torch.matmul(tar_tri_ema, queue.transpose(1, 0).detach()) # update memory with torch.no_grad(): q1 = q1.detach() out_ids = torch.arange(batchSize).cuda() out_ids += self.index out_ids = torch.fmod(out_ids, self.queueSize-self.sour_numclass) out_ids = (out_ids+self.sour_numclass).long() self.memory.index_copy_(0, out_ids, q1) self.index_memory.index_copy_(0, out_ids, index + self.sour_numclass) self.uncer.index_copy_(0, out_ids, uncer) self.index = (self.index + batchSize) % (self.queueSize-self.sour_numclass) for x, y in zip(q2, sour_labels): self.memory[y] = self.momentum * self.memory[y] + (1. - self.momentum) * x self.memory[y] /= self.memory[y].norm() return loss_q1, loss_q2, None, None

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/memorybank/NCECriterion.py

import torch from torch import nn import torch.nn.functional as F eps = 1e-7 class NCECriterion(nn.Module): """ Eq. (12): L_{memorybank} """ def __init__(self, n_data): super(NCECriterion, self).__init__() self.n_data = n_data def forward(self, x): bsz = x.shape[0] m = x.size(1) - 1 # noise distribution Pn = 1 / float(self.n_data) # loss for positive pair P_pos = x.select(1, 0) log_D1 = torch.div(P_pos, P_pos.add(m * Pn + eps)).log_() # loss for K negative pair P_neg = x.narrow(1, 1, m) log_D0 = torch.div(P_neg.clone().fill_(m * Pn), P_neg.add(m * Pn + eps)).log_() loss = - (log_D1.sum(0) + log_D0.view(-1, 1).sum(0)) / bsz return loss class NCESoftmaxLoss(nn.Module): """Softmax cross-entropy loss (a.k.a., info-memorybank loss in CPC paper)""" def __init__(self): super(NCESoftmaxLoss, self).__init__() self.criterion = nn.CrossEntropyLoss() def forward(self, x, is_pos=None): bsz = x.shape[0] x = x.squeeze() label = torch.zeros([bsz]).cuda().long() loss = self.criterion(x, label) return loss class MultiSoftmaxLoss(nn.Module): def __init__(self): super().__init__() # self.criterion = nn.KLDivLoss(reduction='batchmean') # self.criterion = nn.CrossEntropyLoss() # self.criterion = nn.NLLLoss(reduction='mean') def forward(self, x, is_neg): bsz = x.shape[0] # ce_loss = self.criterion(x, torch.zeros([bsz]).cuda().long()) x = x.squeeze() x = torch.exp(x) is_neg = is_neg.float() is_need = torch.cat((torch.ones([bsz, 1], dtype=torch.float).cuda(), is_neg), dim=1) neg_div = (x * is_need).sum(dim=1, keepdim=True) x_logit = x[:,0] / neg_div x_logit = -torch.log(x_logit) loss = x_logit.mean() # x_mask = x_logit * is_pos.float() # num_pos = is_pos.sum(dim=1, keepdim=True).float() # x_mask = x_mask / num_pos # loss = x_logit.sum(dim=1).mean(dim=0) return loss # loss = 0 # for i in range(bsz): # tmp_loss = 0 # pos_inds = torch.where(is_pos[i] == 1)[0].tolist() # num_pos = len(pos_inds) # for j in pos_inds: # tmp_loss -= torch.log(x[i, j] / (neg_div[i][0] + x[i, j])) # loss += (tmp_loss / num_pos) # loss = loss / bsz # # print(loss) # print(fast_loss) # from ipdb import set_trace; set_trace() # print(ce_loss) # print(loss) # def forward(self, x, is_pos): # is_pos = is_pos.float() # bsz = x.shape[0] # x = x.squeeze() # # label = torch.zeros([bsz]).cuda().long() # # loss = self.criterion1(x, ce_label) # # # from ipdb import set_trace; set_trace() # # is_neg = 1 - is_pos[:, 1:] # x = F.softmax(x, dim=1) # x = (x * is_pos).sum(dim=1, keepdim=True) # # neg_logit = (x * is_neg) # # x = torch.cat((pos_logit, x[:, 1:]), dim=1) # [bsz, 16385] # # x = torch.log(x) # # loss = self.criterion(x.log(), label) # return loss # x = F.softmax(x, dim=1) # label = torch.cat((torch.ones([bsz, 1], dtype=torch.float32).cuda(), is_pos), dim=1) # (bsz, dim) # label = F.softmax(label, dim=1) # label = label / label.sum(dim=1, keepdim=True) # loss = torch.sum(x * torch.log(1e-9 + x / (label + 1e-9)), dim=1).mean(dim=0) # loss = torch.sum(x * (1e-9 + torch.log(x) - torch.log(label + 1e-9)), dim=1).mean(dim=0) # from ipdb import set_trace; set_trace() # loss = self.criterion(x, label) # return loss

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/models/resnet_multi.py

from __future__ import absolute_import from torch import nn from torch.nn import functional as F from torch.nn import Parameter from torch.nn import init import torchvision import torch from ..layers import ( IBN, Non_local, get_norm, ) from .gem_pooling import GeneralizedMeanPoolingP __all__ = ['ResNet', 'resnet50_multi'] class ResNet(nn.Module): __factory = { 18: torchvision.models.resnet18, 34: torchvision.models.resnet34, 50: torchvision.models.resnet50, 101: torchvision.models.resnet101, 152: torchvision.models.resnet152, } def __init__(self, depth, mb_h=2048, NL=False, pretrained=True, cut_at_pooling=False, num_features=0, norm=False, dropout=0, num_classes=None,sour_class=751): super(ResNet, self).__init__() self.pretrained = pretrained self.depth = depth self.cut_at_pooling = cut_at_pooling # Construct base (pretrained) resnet if depth not in ResNet.__factory: raise KeyError("Unsupported depth:", depth) resnet = ResNet.__factory[depth](pretrained=pretrained) resnet.layer4[0].conv2.stride = (1,1) resnet.layer4[0].downsample[0].stride = (1,1) self.base = nn.Sequential( resnet.conv1, resnet.bn1, resnet.maxpool) # no relu self.layer1=resnet.layer1 self.layer2=resnet.layer2 self.layer3=resnet.layer3 self.layer4=resnet.layer4 layers= {34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], }[depth] non_layers = {34: [3, 4, 6, 3], 50: [0, 2, 3, 0], 101: [0, 2, 9, 0]}[depth] num_splits=1 if NL: self._build_nonlocal(layers, non_layers, 'BN', num_splits) else: self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = [] # print("w/o GeneralizedMeanPoolingP") # self.gap = nn.AdaptiveAvgPool2d(1) print("GeneralizedMeanPoolingP") self.gap = GeneralizedMeanPoolingP(3) self.memorybank_fc = nn.Linear(2048, mb_h) self.mbn=nn.BatchNorm1d(mb_h) init.kaiming_normal_(self.memorybank_fc.weight, mode='fan_out') init.constant_(self.memorybank_fc.bias, 0) # self.memorybank_fc = nn.Sequential( # nn.Linear(2048, 512, bias=True), # nn.BatchNorm1d(512), # nn.LeakyReLU(), # nn.Linear(512, 128, bias=False), # nn.BatchNorm1d(128) # ) if not self.cut_at_pooling: self.num_features = num_features self.norm = norm self.dropout = dropout self.has_embedding = num_features > 0 self.num_classes = num_classes out_planes = resnet.fc.in_features # Append new layers if self.has_embedding: self.feat = nn.Linear(out_planes, self.num_features) self.feat_bn = nn.BatchNorm1d(self.num_features) init.kaiming_normal_(self.feat.weight, mode='fan_out') init.constant_(self.feat.bias, 0) else: # Change the num_features to CNN output channels self.num_features = out_planes self.feat_bn = nn.BatchNorm1d(self.num_features) self.feat_bn_3 = nn.BatchNorm1d(1024) self.feat_bn.bias.requires_grad_(False) self.feat_bn_3.bias.requires_grad_(False) if self.dropout > 0: self.drop = nn.Dropout(self.dropout) if self.num_classes is not None: for i,num_cluster in enumerate(self.num_classes): exec("self.classifier{}_{} = nn.Linear(self.num_features, {}, bias=False)".format(i,num_cluster,num_cluster)) exec("init.normal_(self.classifier{}_{}.weight, std=0.001)".format(i,num_cluster)) for i,num_cluster in enumerate(self.num_classes): exec("self.classifier3_{}_{} = nn.Linear(1024, {}, bias=False)".format(i,num_cluster,num_cluster)) exec("init.normal_(self.classifier3_{}_{}.weight, std=0.001)".format(i,num_cluster)) # self.weight = Parameter(torch.FloatTensor(self.num_classes[0],self.num_features)) # nn.init.xavier_uniform_(self.weight) # self.weight3 = Parameter(torch.FloatTensor(self.num_classes[0],1024)) # nn.init.xavier_uniform_(self.weight3) # sour_class=751 # self.classifier_ml = nn.Sequential( # nn.Linear(self.num_features, 512, bias=True), # nn.BatchNorm1d(512), # nn.LeakyReLU(), # nn.Linear(512, sour_class, bias=False), # nn.BatchNorm1d(sour_class) # ) if not pretrained: self.reset_params() def _build_nonlocal(self, layers, non_layers, bn_norm, num_splits): self.NL_1 = nn.ModuleList( [Non_local(256, bn_norm, num_splits) for _ in range(non_layers[0])]) self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])]) self.NL_2 = nn.ModuleList( [Non_local(512, bn_norm, num_splits) for _ in range(non_layers[1])]) self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])]) self.NL_3 = nn.ModuleList( [Non_local(1024, bn_norm, num_splits) for _ in range(non_layers[2])]) self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])]) self.NL_4 = nn.ModuleList( [Non_local(2048, bn_norm, num_splits) for _ in range(non_layers[3])]) self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])]) def forward(self, x, feature_withbn=False, training=False, cluster=False): x = self.base(x) NL1_counter = 0 if len(self.NL_1_idx) == 0: self.NL_1_idx = [-1] for i in range(len(self.layer1)): x = self.layer1[i](x) if i == self.NL_1_idx[NL1_counter]: _, C, H, W = x.shape x = self.NL_1[NL1_counter](x) NL1_counter += 1 # Layer 2 NL2_counter = 0 if len(self.NL_2_idx) == 0: self.NL_2_idx = [-1] for i in range(len(self.layer2)): x = self.layer2[i](x) if i == self.NL_2_idx[NL2_counter]: _, C, H, W = x.shape x = self.NL_2[NL2_counter](x) NL2_counter += 1 # Layer 3 x3=x NL3_counter = 0 if len(self.NL_3_idx) == 0: self.NL_3_idx = [-1] for i in range(len(self.layer3)): x3 = self.layer3[i](x3) if i == self.NL_3_idx[NL3_counter]: _, C, H, W = x3.shape x3 = self.NL_3[NL3_counter](x3) NL3_counter += 1 # Layer 4 x4=x3 NL4_counter = 0 if len(self.NL_4_idx) == 0: self.NL_4_idx = [-1] for i in range(len(self.layer4)): x4 = self.layer4[i](x4) if i == self.NL_4_idx[NL4_counter]: _, C, H, W = x.shape x4 = self.NL_4[NL4_counter](x4) NL4_counter += 1 x = self.gap(x4) x3 = self.gap(x3) x = x.view(x.size(0), -1) x3 = x3.view(x3.size(0), -1) bn_x = self.feat_bn(x) bn_x3 = self.feat_bn_3(x3) # if training is False: # bn_x = F.normalize(bn_x) # return bn_x if self.dropout > 0:#FALSE bn_x = self.drop(bn_x) prob = [] prob_3=[] if self.num_classes is not None: for i,num_cluster in enumerate(self.num_classes): exec("prob.append(self.classifier{}_{}(bn_x))".format(i,num_cluster)) for i, num_cluster in enumerate(self.num_classes): exec("prob_3.append(self.classifier3_{}_{}(bn_x3))".format(i, num_cluster)) else: return x, bn_x if feature_withbn:#False return bn_x, prob mb_x = self.mbn(self.memorybank_fc(bn_x)) # ml_x = self.classifier_ml(bn_x) # prob = [F.linear(F.normalize(bn_x), F.normalize(self.weight))] # prob_3 = [F.linear(F.normalize(bn_x3), F.normalize(self.weight3))] if training is False: bn_x = F.normalize(bn_x) return bn_x return x, prob, mb_x, None, prob_3, x3 def reset_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) resnet = ResNet.__factory[self.depth](pretrained=self.pretrained) self.base[0].load_state_dict(resnet.conv1.state_dict()) self.base[1].load_state_dict(resnet.bn1.state_dict()) self.base[2].load_state_dict(resnet.maxpool.state_dict()) self.base[3].load_state_dict(resnet.layer1.state_dict()) self.base[4].load_state_dict(resnet.layer2.state_dict()) self.base[5].load_state_dict(resnet.layer3.state_dict()) self.base[6].load_state_dict(resnet.layer4.state_dict()) def resnet50_multi(mb_h,sour_class,**kwargs): return ResNet(50, mb_h=mb_h, sour_class=sour_class,**kwargs) def resnet50_multi_sbs(mb_h,sour_class,**kwargs): return ResNet(50, mb_h=mb_h,sour_class=sour_class,NL=True, **kwargs)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/models/memory_bank.py

import torch from torch import nn from torch.nn import functional as F import math from numpy.testing import assert_almost_equal def normalize(x, axis=-1): """Normalizing to unit length along the specified dimension. Args: x: pytorch Variable Returns: x: pytorch Variable, same shape as input """ x = 1. * x / (torch.norm(x, 2, axis, keepdim=True).expand_as(x) + 1e-12) return x def sigmoid(tensor, temp=1.0): exponent = -tensor / temp exponent = torch.clamp(exponent, min=-50, max=50) y = 1.0 / (1.0 + torch.exp(exponent)) return y def logsumexp(value, weight = 1, dim=None, keepdim=False): """Numerically stable implementation of the operation value.exp().sum(dim, keepdim).log() """ # TODO: torch.max(value, dim=None) threw an error at time of writing if dim is not None: m, _ = torch.max(value, dim=dim, keepdim=True) value0 = value - m if keepdim is False: m = m.squeeze(dim) return m + torch.log(torch.sum(weight * torch.exp(value0), dim=dim, keepdim=keepdim)) else: m = torch.max(value) sum_exp = torch.sum(weight * torch.exp(value - m)) return m + torch.log(sum_exp) class onlinememory(nn.Module): """Fixed-size queue with momentum encoder""" def __init__(self, inputSize, sour_numclass, K, index2label, choice_c=1, T=0.07, use_softmax=False, cluster_num=0): super(onlinememory, self).__init__() self.inputSize = inputSize self.sour_numclass = sour_numclass self.queueSize = K self.T = T self.index = 0 self.use_softmax = use_softmax self.register_buffer('params', torch.tensor([-1])) stdv = 1. / math.sqrt(inputSize / 3) self.register_buffer('memory', torch.rand(self.queueSize, inputSize).mul_(2 * stdv).add_(-stdv)) self.register_buffer('index_memory', torch.ones(self.queueSize, dtype=torch.long).fill_(-1)) self.register_buffer('uncer', torch.ones(self.queueSize, dtype=torch.float).fill_(1)) print('Using queue shape: ({},{})'.format(self.queueSize, inputSize)) # self.register_buffer('sour_memory', torch.rand(self.sour_numclass, inputSize).mul_(2 * stdv).add_(-stdv)) # self.register_buffer('sour_index_memory', torch.ones(self.sour_numclass, dtype=torch.long).fill_(-1)) # print('Using queue shape: ({},{})'.format(self.sour_numclass, inputSize)) self.choice_c = choice_c self.index_pl = -1 # 3-cluster_num if cluster_num<=3 else 0 self.index2label = index2label self.m = 0.25 self.gamma = 128 self.momentum = 0.2 ################ # his loss num_steps = 151 self.step = 2 / (num_steps - 1) self.eps = 1 / num_steps self.t = torch.arange(-1, 1 + self.step, self.step).view(-1, 1).cuda() self.tsize = self.t.size()[0] ############### # smooth ap loss self.anneal = 0.01 self.num_id = 16 def memo_circle_loss(self, index, q1, uncer): batchSize = q1.shape[0] # import ipdb;ipdb.set_trace() pseudo_label = torch.tensor([self.index2label[self.choice_c][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.queueSize) memory_label_list=[] for i in self.index_memory: try: if i.item() == -1: memory_label_list.append(-1) else: memory_label_list.append(self.index2label[self.choice_c][i.item()]) except: print("error index{}".format(i.item())) memory_label = torch.tensor(memory_label_list,dtype=torch.long).cuda() # memory_label = torch.tensor( # [self.index2label[self.choice_c][i.item()] if i.item() != -1 else -1 for i in self.index_memory], # dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.queueSize) is_pos = pseudo_label.eq(memory_label).float() is_neg = pseudo_label.ne(memory_label).float() queue = self.memory.clone() l_logist = torch.matmul(queue.detach(), (q1).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() sim_mat = l_logist s_p = sim_mat * is_pos s_n = sim_mat * is_neg if uncer is not None: exp_variance = (uncer.unsqueeze(1).expand(batchSize, self.queueSize) +self.uncer.clone().unsqueeze(0).expand(batchSize, self.queueSize)) / 2.0 else: exp_variance=1 alpha_p = F.relu(-s_p.detach() + 1 + self.m) alpha_n = F.relu(s_n.detach() + self.m) delta_p = 1 - self.m delta_n = self.m logit_p = - self.gamma * alpha_p * (s_p - delta_p) logit_n = self.gamma * alpha_n * (s_n - delta_n) # ,weight=exp_variance loss = (F.softplus(logsumexp(logit_p - 99999.0 * is_neg, weight=exp_variance, dim=1) + logsumexp(logit_n - 99999.0 * is_pos, weight=exp_variance, dim=1))).mean()/ 18.0 return loss def memo_center_circle_loss(self, index, q1): batchSize = q1.shape[0] pseudo_label = torch.tensor([self.index2label[self.choice_c][i.item()] for i in index], dtype=torch.long).cuda() pseudo_label = pseudo_label.unsqueeze(1).expand(batchSize, self.sour_numclass) # pseudo_label = index.expand(batchSize, self.sour_numclass) memory_label = torch.tensor( [self.index2label[self.choice_c][i] for i in range(self.sour_numclass)], dtype=torch.long).cuda() memory_label = memory_label.unsqueeze(0).expand(batchSize, self.sour_numclass) is_pos = pseudo_label.eq(memory_label).float() is_neg = pseudo_label.ne(memory_label).float() queue = self.memory[:self.sour_numclass, :].clone() l_logist = torch.matmul(queue.detach(), (q1).transpose(1, 0)) l_logist = l_logist.transpose(0, 1).contiguous() sim_mat = l_logist s_p = sim_mat * is_pos s_n = sim_mat * is_neg alpha_p = F.relu(-s_p.detach() + 1 + self.m) alpha_n = F.relu(s_n.detach() + self.m) delta_p = 1 - self.m delta_n = self.m logit_p = - self.gamma * alpha_p * (s_p - delta_p) logit_n = self.gamma * alpha_n * (s_n - delta_n) loss = F.softplus(logsumexp(logit_p - 99999.0 * is_neg, dim=1) + logsumexp(logit_n - 99999.0 * is_pos, dim=1)).mean() / 18.0 return loss def forward(self, q1, q2, index, tar_tri, tar_tri_ema, sour_labels, uncer=None, epoch=0): batchSize = q1.shape[0] # tar_tri = normalize(tar_tri, axis=-1) q1 = normalize(q1, axis=-1) q2 = normalize(q2, axis=-1) loss_q1 = self.memo_circle_loss(index + self.sour_numclass, q1, uncer) loss_q2 = self.memo_center_circle_loss(sour_labels, q2) with torch.no_grad(): q1 = q1.detach() out_ids = torch.arange(batchSize).cuda() out_ids += self.index out_ids = torch.fmod(out_ids, self.queueSize - self.sour_numclass) out_ids = (out_ids + self.sour_numclass).long() self.memory.index_copy_(0, out_ids, q1) self.index_memory.index_copy_(0, out_ids, index + self.sour_numclass) if uncer is not None: self.uncer.index_copy_(0, out_ids, uncer) self.index = (self.index + batchSize) % (self.queueSize - self.sour_numclass) for x, y in zip(q2, sour_labels): self.memory[y] = self.momentum * self.memory[y] + (1. - self.momentum) * x self.memory[y] /= self.memory[y].norm() return loss_q1, loss_q2, None, None

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/models/resnet.py

from __future__ import absolute_import from torch import nn from torch.nn import functional as F from torch.nn import init import torchvision import torch from ..layers import ( IBN, Non_local, get_norm, ) from .gem_pooling import GeneralizedMeanPoolingP __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet50_sbs'] class ResNet(nn.Module): __factory = { 18: torchvision.models.resnet18, 34: torchvision.models.resnet34, 50: torchvision.models.resnet50, 101: torchvision.models.resnet101, 152: torchvision.models.resnet152, } def __init__(self, depth, mb_h=2048, with_nl=False,pretrained=True, cut_at_pooling=False, num_features=0, norm=False, dropout=0, num_classes=None, sour_class=751): super(ResNet, self).__init__() self.pretrained = pretrained self.depth = depth self.cut_at_pooling = cut_at_pooling # Construct base (pretrained) resnet if depth not in ResNet.__factory: raise KeyError("Unsupported depth:", depth) resnet = ResNet.__factory[depth](pretrained=pretrained) resnet.layer4[0].conv2.stride = (1,1) resnet.layer4[0].downsample[0].stride = (1,1) self.base = nn.Sequential( resnet.conv1, resnet.bn1, resnet.maxpool) # no relu self.layer1=resnet.layer1 self.layer2=resnet.layer2 self.layer3=resnet.layer3 self.layer4=resnet.layer4 layers= {34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], }[depth] non_layers = {34: [3, 4, 6, 3], 50: [0, 2, 3, 0], 101: [0, 2, 9, 0]}[depth] num_splits=1 if with_nl: self._build_nonlocal(layers, non_layers, 'BN', num_splits) else: self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = [] # print("w/o GeneralizedMeanPoolingP") # self.gap = nn.AdaptiveAvgPool2d(1) print("GeneralizedMeanPoolingP") self.gap = GeneralizedMeanPoolingP(3) self.memorybank_fc = nn.Linear(2048, mb_h) self.mbn=nn.BatchNorm1d(mb_h) init.kaiming_normal_(self.memorybank_fc.weight, mode='fan_out') init.constant_(self.memorybank_fc.bias, 0) # self.memorybank_fc = nn.Sequential( # nn.Linear(2048, 512, bias=True), # nn.BatchNorm1d(512), # nn.LeakyReLU(), # nn.Linear(512, 128, bias=False), # nn.BatchNorm1d(128) # ) if not self.cut_at_pooling: self.num_features = num_features self.norm = norm self.dropout = dropout self.has_embedding = num_features > 0 self.num_classes = num_classes out_planes = resnet.fc.in_features # Append new layers if self.has_embedding: self.feat = nn.Linear(out_planes, self.num_features) self.feat_bn = nn.BatchNorm1d(self.num_features) init.kaiming_normal_(self.feat.weight, mode='fan_out') init.constant_(self.feat.bias, 0) else: # Change the num_features to CNN output channels self.num_features = out_planes self.feat_bn = nn.BatchNorm1d(self.num_features) self.feat_bn.bias.requires_grad_(False) if self.dropout > 0: self.drop = nn.Dropout(self.dropout) if self.num_classes is not None: for i,num_cluster in enumerate(self.num_classes): exec("self.classifier{}_{} = nn.Linear(self.num_features, {}, bias=False)".format(i,num_cluster,num_cluster)) exec("init.normal_(self.classifier{}_{}.weight, std=0.001)".format(i,num_cluster)) if not pretrained: self.reset_params() def _build_nonlocal(self, layers, non_layers, bn_norm, num_splits): self.NL_1 = nn.ModuleList( [Non_local(256, bn_norm, num_splits) for _ in range(non_layers[0])]) self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])]) self.NL_2 = nn.ModuleList( [Non_local(512, bn_norm, num_splits) for _ in range(non_layers[1])]) self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])]) self.NL_3 = nn.ModuleList( [Non_local(1024, bn_norm, num_splits) for _ in range(non_layers[2])]) self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])]) self.NL_4 = nn.ModuleList( [Non_local(2048, bn_norm, num_splits) for _ in range(non_layers[3])]) self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])]) def forward(self, x, feature_withbn=False, training=False, cluster=False): x = self.base(x) NL1_counter = 0 if len(self.NL_1_idx) == 0: self.NL_1_idx = [-1] for i in range(len(self.layer1)): x = self.layer1[i](x) if i == self.NL_1_idx[NL1_counter]: _, C, H, W = x.shape x = self.NL_1[NL1_counter](x) NL1_counter += 1 # Layer 2 NL2_counter = 0 if len(self.NL_2_idx) == 0: self.NL_2_idx = [-1] for i in range(len(self.layer2)): x = self.layer2[i](x) if i == self.NL_2_idx[NL2_counter]: _, C, H, W = x.shape x = self.NL_2[NL2_counter](x) NL2_counter += 1 # Layer 3 NL3_counter = 0 if len(self.NL_3_idx) == 0: self.NL_3_idx = [-1] for i in range(len(self.layer3)): x = self.layer3[i](x) if i == self.NL_3_idx[NL3_counter]: _, C, H, W = x.shape x = self.NL_3[NL3_counter](x) NL3_counter += 1 # Layer 4 NL4_counter = 0 if len(self.NL_4_idx) == 0: self.NL_4_idx = [-1] for i in range(len(self.layer4)): x = self.layer4[i](x) if i == self.NL_4_idx[NL4_counter]: _, C, H, W = x.shape x = self.NL_4[NL4_counter](x) NL4_counter += 1 x = self.gap(x) x = x.view(x.size(0), -1) if self.cut_at_pooling:return x#FALSE if self.has_embedding: bn_x = self.feat_bn(self.feat(x))#FALSE else: bn_x = self.feat_bn(x)#1 if training is False: bn_x = F.normalize(bn_x) return bn_x if self.norm:#FALSE bn_x = F.normalize(bn_x) elif self.has_embedding:#FALSE bn_x = F.relu(bn_x) if self.dropout > 0:#FALSE bn_x = self.drop(bn_x) prob = [] if self.num_classes is not None: for i,num_cluster in enumerate(self.num_classes): exec("prob.append(self.classifier{}_{}(bn_x))".format(i,num_cluster)) else: return x, bn_x if feature_withbn:#False return bn_x, prob mb_x = self.mbn(self.memorybank_fc(bn_x)) return x, prob, mb_x, None def reset_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) resnet = ResNet.__factory[self.depth](pretrained=self.pretrained) self.base[0].load_state_dict(resnet.conv1.state_dict()) self.base[1].load_state_dict(resnet.bn1.state_dict()) self.base[2].load_state_dict(resnet.maxpool.state_dict()) self.base[3].load_state_dict(resnet.layer1.state_dict()) self.base[4].load_state_dict(resnet.layer2.state_dict()) self.base[5].load_state_dict(resnet.layer3.state_dict()) self.base[6].load_state_dict(resnet.layer4.state_dict()) def resnet18(**kwargs): return ResNet(18, **kwargs) def resnet34(**kwargs): return ResNet(34, **kwargs) def resnet50(mb_h,sour_class,**kwargs): return ResNet(50, mb_h=mb_h, sour_class=sour_class, **kwargs) def resnet50_sbs(mb_h,sour_class,**kwargs): return ResNet(50, mb_h=mb_h, sour_class=sour_class,with_nl=True, **kwargs) def resnet101(**kwargs): return ResNet(101, **kwargs) def resnet152(**kwargs): return ResNet(152, **kwargs)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/models/gem_pooling.py

# encoding: utf-8 """ @author: l1aoxingyu @contact: sherlockliao01@gmail.com """ import torch import torch.nn.functional as F from torch import nn class GeneralizedMeanPooling(nn.Module): r"""Applies a 2D power-average adaptive pooling over an input signal composed of several input planes. The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)` - At p = infinity, one gets Max Pooling - At p = 1, one gets Average Pooling The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Args: output_size: the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a square image H x H H and W can be either a ``int``, or ``None`` which means the size will be the same as that of the input. """ def __init__(self, norm, output_size=1, eps=1e-6): super(GeneralizedMeanPooling, self).__init__() assert norm > 0 self.p = float(norm) self.output_size = output_size self.eps = eps def forward(self, x): x = x.clamp(min=self.eps).pow(self.p) return torch.nn.functional.adaptive_avg_pool2d(x, self.output_size).pow(1. / self.p) def __repr__(self): return self.__class__.__name__ + '(' \ + str(self.p) + ', ' \ + 'output_size=' + str(self.output_size) + ')' class GeneralizedMeanPoolingP(GeneralizedMeanPooling): """ Same, but norm is trainable """ def __init__(self, norm=3, output_size=1, eps=1e-6): super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps) self.p = nn.Parameter(torch.ones(1) * norm)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/models/dsbn.py

import torch import torch.nn as nn # Domain-specific BatchNorm class DSBN2d(nn.Module): def __init__(self, planes): super(DSBN2d, self).__init__() self.num_features = planes self.BN_S = nn.BatchNorm2d(planes) self.BN_T = nn.BatchNorm2d(planes) def forward(self, x): if (not self.training): return self.BN_T(x) bs = x.size(0) assert (bs%2==0) split = torch.split(x, int(bs/2), 0) out1 = self.BN_S(split[0].contiguous()) out2 = self.BN_T(split[1].contiguous()) out = torch.cat((out1, out2), 0) return out class DSBN1d(nn.Module): def __init__(self, planes): super(DSBN1d, self).__init__() self.num_features = planes self.BN_S = nn.BatchNorm1d(planes) self.BN_T = nn.BatchNorm1d(planes) def forward(self, x): if (not self.training): return self.BN_T(x) bs = x.size(0) assert (bs%2==0) split = torch.split(x, int(bs/2), 0) out1 = self.BN_S(split[0].contiguous()) out2 = self.BN_T(split[1].contiguous()) out = torch.cat((out1, out2), 0) return out def convert_dsbn(model): for _, (child_name, child) in enumerate(model.named_children()): # if 'NL_' in child_name:continue if isinstance(child, nn.BatchNorm2d): m = DSBN2d(child.num_features) m.BN_S.load_state_dict(child.state_dict()) m.BN_T.load_state_dict(child.state_dict()) setattr(model, child_name, m) elif isinstance(child, nn.BatchNorm1d): m = DSBN1d(child.num_features) m.BN_S.load_state_dict(child.state_dict()) m.BN_T.load_state_dict(child.state_dict()) setattr(model, child_name, m) else: convert_dsbn(child) def convert_bn(model, use_target=True): for _, (child_name, child) in enumerate(model.named_children()): assert(not next(model.parameters()).is_cuda) if isinstance(child, DSBN2d): m = nn.BatchNorm2d(child.num_features) if use_target: m.load_state_dict(child.BN_T.state_dict()) else: m.load_state_dict(child.BN_S.state_dict()) setattr(model, child_name, m) elif isinstance(child, DSBN1d): m = nn.BatchNorm1d(child.num_features) if use_target: m.load_state_dict(child.BN_T.state_dict()) else: m.load_state_dict(child.BN_S.state_dict()) setattr(model, child_name, m) else: convert_bn(child, use_target=use_target)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/layers/batch_norm.py

# encoding: utf-8 """ @author: liaoxingyu @contact: sherlockliao01@gmail.com """ import logging import torch import torch.nn.functional as F from torch import nn __all__ = [ "BatchNorm", "IBN", "GhostBatchNorm", "FrozenBatchNorm", "SyncBatchNorm", "get_norm", ] class BatchNorm(nn.BatchNorm2d): def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze=False, bias_freeze=False, weight_init=1.0, bias_init=0.0): super().__init__(num_features, eps=eps, momentum=momentum) if weight_init is not None: self.weight.data.fill_(weight_init) if bias_init is not None: self.bias.data.fill_(bias_init) self.weight.requires_grad_(not weight_freeze) self.bias.requires_grad_(not bias_freeze) class SyncBatchNorm(nn.SyncBatchNorm): def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze=False, bias_freeze=False, weight_init=1.0, bias_init=0.0): super().__init__(num_features, eps=eps, momentum=momentum) if weight_init is not None: self.weight.data.fill_(weight_init) if bias_init is not None: self.bias.data.fill_(bias_init) self.weight.requires_grad_(not weight_freeze) self.bias.requires_grad_(not bias_freeze) class IBN(nn.Module): def __init__(self, planes, bn_norm, num_splits): super(IBN, self).__init__() half1 = int(planes / 2) self.half = half1 half2 = planes - half1 self.IN = nn.InstanceNorm2d(half1, affine=True) self.BN = get_norm(bn_norm, half2, num_splits) def forward(self, x): split = torch.split(x, self.half, 1) out1 = self.IN(split[0].contiguous()) out2 = self.BN(split[1].contiguous()) out = torch.cat((out1, out2), 1) return out class GhostBatchNorm(BatchNorm): def __init__(self, num_features, num_splits=1, **kwargs): super().__init__(num_features, **kwargs) self.num_splits = num_splits self.register_buffer('running_mean', torch.zeros(num_features)) self.register_buffer('running_var', torch.ones(num_features)) def forward(self, input): N, C, H, W = input.shape if self.training or not self.track_running_stats: self.running_mean = self.running_mean.repeat(self.num_splits) self.running_var = self.running_var.repeat(self.num_splits) outputs = F.batch_norm( input.view(-1, C * self.num_splits, H, W), self.running_mean, self.running_var, self.weight.repeat(self.num_splits), self.bias.repeat(self.num_splits), True, self.momentum, self.eps).view(N, C, H, W) self.running_mean = torch.mean(self.running_mean.view(self.num_splits, self.num_features), dim=0) self.running_var = torch.mean(self.running_var.view(self.num_splits, self.num_features), dim=0) return outputs else: return F.batch_norm( input, self.running_mean, self.running_var, self.weight, self.bias, False, self.momentum, self.eps) class FrozenBatchNorm(BatchNorm): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. It contains non-trainable buffers called "weight" and "bias", "running_mean", "running_var", initialized to perform identity transformation. The pre-trained backbone models from Caffe2 only contain "weight" and "bias", which are computed from the original four parameters of BN. The affine transform `x * weight + bias` will perform the equivalent computation of `(x - running_mean) / sqrt(running_var) * weight + bias`. When loading a backbone model from Caffe2, "running_mean" and "running_var" will be left unchanged as identity transformation. Other pre-trained backbone models may contain all 4 parameters. The forward is implemented by `F.batch_norm(..., training=False)`. """ _version = 3 def __init__(self, num_features, eps=1e-5): super().__init__(num_features, weight_freeze=True, bias_freeze=True) self.num_features = num_features self.eps = eps def forward(self, x): if x.requires_grad: # When gradients are needed, F.batch_norm will use extra memory # because its backward op computes gradients for weight/bias as well. scale = self.weight * (self.running_var + self.eps).rsqrt() bias = self.bias - self.running_mean * scale scale = scale.reshape(1, -1, 1, 1) bias = bias.reshape(1, -1, 1, 1) return x * scale + bias else: # When gradients are not needed, F.batch_norm is a single fused op # and provide more optimization opportunities. return F.batch_norm( x, self.running_mean, self.running_var, self.weight, self.bias, training=False, eps=self.eps, ) def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): version = local_metadata.get("version", None) if version is None or version < 2: # No running_mean/var in early versions # This will silent the warnings if prefix + "running_mean" not in state_dict: state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean) if prefix + "running_var" not in state_dict: state_dict[prefix + "running_var"] = torch.ones_like(self.running_var) if version is not None and version < 3: logger = logging.getLogger(__name__) logger.info("FrozenBatchNorm {} is upgraded to version 3.".format(prefix.rstrip("."))) # In version < 3, running_var are used without +eps. state_dict[prefix + "running_var"] -= self.eps super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def __repr__(self): return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps) @classmethod def convert_frozen_batchnorm(cls, module): """ Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. Args: module (torch.nn.Module): Returns: If module is BatchNorm/SyncBatchNorm, returns a new module. Otherwise, in-place convert module and return it. Similar to convert_sync_batchnorm in https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py """ bn_module = nn.modules.batchnorm bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) res = module if isinstance(module, bn_module): res = cls(module.num_features) if module.affine: res.weight.data = module.weight.data.clone().detach() res.bias.data = module.bias.data.clone().detach() res.running_mean.data = module.running_mean.data res.running_var.data = module.running_var.data res.eps = module.eps else: for name, child in module.named_children(): new_child = cls.convert_frozen_batchnorm(child) if new_child is not child: res.add_module(name, new_child) return res def get_norm(norm, out_channels, num_splits=1, **kwargs): """ Args: norm (str or callable): Returns: nn.Module or None: the normalization layer """ if isinstance(norm, str): if len(norm) == 0: return None norm = { "BN": BatchNorm(out_channels, **kwargs), "GhostBN": GhostBatchNorm(out_channels, num_splits, **kwargs), "FrozenBN": FrozenBatchNorm(out_channels), "GN": nn.GroupNorm(32, out_channels), "syncBN": SyncBatchNorm(out_channels, **kwargs), }[norm] return norm

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/layers/non_local.py

# encoding: utf-8 import torch from torch import nn from .batch_norm import get_norm class Non_local(nn.Module): def __init__(self, in_channels, bn_norm, num_splits, reduc_ratio=2): super(Non_local, self).__init__() self.in_channels = in_channels self.inter_channels = reduc_ratio // reduc_ratio self.g = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) self.W = nn.Sequential( nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0), get_norm(bn_norm, self.in_channels, num_splits), ) nn.init.constant_(self.W[1].weight, 0.0) nn.init.constant_(self.W[1].bias, 0.0) self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) def forward(self, x): ''' :param x: (b, t, h, w) :return x: (b, t, h, w) ''' batch_size = x.size(0) g_x = self.g(x).view(batch_size, self.inter_channels, -1) g_x = g_x.permute(0, 2, 1) theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) theta_x = theta_x.permute(0, 2, 1) phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) f = torch.matmul(theta_x, phi_x) N = f.size(-1) f_div_C = f / N y = torch.matmul(f_div_C, g_x) y = y.permute(0, 2, 1).contiguous() y = y.view(batch_size, self.inter_channels, *x.size()[2:]) W_y = self.W(y) z = W_y + x return z

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/layers/__init__.py

# encoding: utf-8 """ @author: liaoxingyu @contact: sherlockliao01@gmail.com """ from torch import nn # from .batch_drop import BatchDrop # from .attention import * from .batch_norm import * # from .context_block import ContextBlock from .non_local import Non_local # from .se_layer import SELayer # from .frn import FRN, TLU # from .activation import * # from .gem_pool import GeneralizedMeanPoolingP, AdaptiveAvgMaxPool2d # from .arcface import Arcface # from .circle import Circle # from .splat import SplAtConv2d class Flatten(nn.Module): def forward(self, input): return input.view(input.size(0), -1)

py

UDAStrongBaseline

UDAStrongBaseline-master/UDAsbs/layers/sync_bn/replicate.py

# -*- coding: utf-8 -*- # File : replicate.py # Author : Jiayuan Mao # Email : maojiayuan@gmail.com # Date : 27/01/2018 # # This file is part of Synchronized-BatchNorm-PyTorch. # https://github.com/vacancy/Synchronized-BatchNorm-PyTorch # Distributed under MIT License. import functools from torch.nn.parallel.data_parallel import DataParallel __all__ = [ 'CallbackContext', 'execute_replication_callbacks', 'DataParallelWithCallback', 'patch_replication_callback' ] class CallbackContext(object): pass def execute_replication_callbacks(modules): """ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign each sub-module with a context (shared among multiple copies of this module on different devices). Through this context, different copies can share some information. We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback of any slave copies. """ master_copy = modules[0] nr_modules = len(list(master_copy.modules())) ctxs = [CallbackContext() for _ in range(nr_modules)] for i, module in enumerate(modules): for j, m in enumerate(module.modules()): if hasattr(m, '__data_parallel_replicate__'): m.__data_parallel_replicate__(ctxs[j], i) class DataParallelWithCallback(DataParallel): """ Data Parallel with a replication callback. An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by original `replicate` function. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) # sync_bn.__data_parallel_replicate__ will be invoked. """ def replicate(self, module, device_ids): modules = super(DataParallelWithCallback, self).replicate(module, device_ids) execute_replication_callbacks(modules) return modules def patch_replication_callback(data_parallel): """ Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) > patch_replication_callback(sync_bn) # this is equivalent to > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) """ assert isinstance(data_parallel, DataParallel) old_replicate = data_parallel.replicate @functools.wraps(old_replicate) def new_replicate(module, device_ids): modules = old_replicate(module, device_ids) execute_replication_callbacks(modules) return modules data_parallel.replicate = new_replicate

py