diff --git a/configs/swingait/swingait3D_B1122C2_CCPG.yaml b/configs/swingait/swingait3D_B1122C2_CCPG.yaml new file mode 100644 index 0000000..4633221 --- /dev/null +++ b/configs/swingait/swingait3D_B1122C2_CCPG.yaml @@ -0,0 +1,100 @@ +data_cfg: + dataset_name: CCPG + dataset_root: /data/CCPG/Released/CCPG-end2end-pkl/ + dataset_partition: ./datasets/CCPG/CCPG.json + num_workers: 1 + data_in_use: [True, False, False, False] + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: CCPG + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 80000 + save_name: SwinGait3D_B1122_C2 + sampler: + batch_shuffle: false + batch_size: 4 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + eval_func: evaluate_CCPG + metric: euc # cos + transform: + - type: BaseSilCuttingTransform + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 1.0 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: SwinGait + Backbone: + mode: p3d + in_channels: 1 + layers: + - 1 + - 1 + - 2 + - 2 + channels: + - 64 + - 128 + bin_num: + - 15 + SeparateBNNecks: + in_channels: 256 + class_num: 100 + parts_num: 16 + +optimizer_cfg: + lr: 0.0003 + solver: AdamW + weight_decay: 0.02 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 100000000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + log_iter: 100 + with_test: false + optimizer_reset: True + restore_ckpt_strict: false + restore_hint: /home/jdy/fanchao/OpenGait_230719/output/CCPG/DeepGaitV2/DeepGaitV2_B1111_C2_P3D/checkpoints/DeepGaitV2_B1111_C2_P3D-60000.pt + save_iter: 20000 + save_name: SwinGait3D_B1122_C2 + sync_BN: true + T_max_iter: 60000 + total_iter: 80000 + sampler: + batch_shuffle: true + batch_size: + - 8 # TripletSampler, batch_size[0] indicates Number of Identity + - 16 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 30 # fixed frames number for training + frames_skip_num: 4 + sample_type: fixed_ordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: Compose + trf_cfg: + - type: RandomPerspective + prob: 0.2 + - type: BaseSilCuttingTransform + - type: RandomHorizontalFlip + prob: 0.2 + - type: RandomRotate + prob: 0.2 + + diff --git a/configs/swingait/swingait3D_B1122C2_SUSTech1K.yaml b/configs/swingait/swingait3D_B1122C2_SUSTech1K.yaml new file mode 100644 index 0000000..451dee2 --- /dev/null +++ b/configs/swingait/swingait3D_B1122C2_SUSTech1K.yaml @@ -0,0 +1,98 @@ +data_cfg: + dataset_name: SUSTech1K + dataset_root: /data/SUSTech1K-Released-pkl/ + dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json + num_workers: 4 + data_in_use: [false, false, false, false, false, false, true, false, false, false, false, false, false, false, false, false] + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: SUSTech1K + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 60000 + save_name: SwinGait3D_B1122_C2 + eval_func: evaluate_indoor_dataset #evaluate_Gait3D + sampler: + batch_shuffle: false + batch_size: 4 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + metric: euc # cos + transform: + - type: BaseSilCuttingTransform + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 1.0 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: SwinGait + Backbone: + mode: p3d + in_channels: 1 + layers: + - 1 + - 1 + - 2 + - 2 + channels: + - 64 + - 128 + bin_num: + - 15 + SeparateBNNecks: + in_channels: 256 + class_num: 250 + parts_num: 16 + +optimizer_cfg: + lr: 0.0003 + solver: AdamW + weight_decay: 0.02 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 100000000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + with_test: true #true + log_iter: 100 + optimizer_reset: True + restore_ckpt_strict: false + restore_hint: /home/jdy/fanchao/OpenGait_230719/output/SUSTech1K/DeepGaitV2/DeepGaitV2_B1111_C2_P3D/checkpoints/DeepGaitV2_B1111_C2_P3D-50000.pt + save_iter: 10000 + save_name: SwinGait3D_B1122_C2 + sync_BN: true + T_max_iter: 50000 + total_iter: 60000 + sampler: + batch_shuffle: true + batch_size: + - 8 # TripletSampler, batch_size[0] indicates Number of Identity + - 8 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 10 # fixed frames number for training + sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: Compose + trf_cfg: + - type: RandomPerspective + prob: 0.2 + - type: BaseSilCuttingTransform + - type: RandomHorizontalFlip + prob: 0.2 + - type: RandomRotate + prob: 0.2 + diff --git a/configs/swingait/swingait3D_B1442C2_GREW.yaml b/configs/swingait/swingait3D_B1442C2_GREW.yaml new file mode 100644 index 0000000..cf09e31 --- /dev/null +++ b/configs/swingait/swingait3D_B1442C2_GREW.yaml @@ -0,0 +1,99 @@ +data_cfg: + dataset_name: GREW + dataset_root: your_path + dataset_partition: ./datasets/GREW/GREW.json + num_workers: 1 + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: GREW + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 200000 + save_name: SwinGait3D_B1442_C2 + sampler: + batch_shuffle: false + batch_size: 4 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + eval_func: GREW_submission + metric: euc # cos + transform: + - type: BaseSilCuttingTransform + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 1.0 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: SwinGait + Backbone: + mode: p3d + in_channels: 1 + layers: + - 1 + - 4 + - 4 + - 2 + channels: + - 64 + - 128 + bin_num: + - 15 + SeparateBNNecks: + in_channels: 256 + class_num: 20000 + parts_num: 16 + +optimizer_cfg: + lr: 0.0003 + solver: AdamW + weight_decay: 0.02 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 100000000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + log_iter: 100 + with_test: false + optimizer_reset: True + restore_ckpt_strict: false + restore_hint: /home/jdy/fanchao/OpenGait_230719/output/GREW/DeepGaitV2/DeepGaitV2_B1441_C2_P3D/checkpoints/DeepGaitV2_B1441_C2_P3D-180000.pt + save_iter: 20000 + save_name: SwinGait3D_B1442_C2 + sync_BN: true + T_max_iter: 150000 + total_iter: 200000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 30 # fixed frames number for training + frames_skip_num: 4 + sample_type: fixed_ordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: Compose + trf_cfg: + - type: RandomPerspective + prob: 0.2 + - type: BaseSilCuttingTransform + - type: RandomHorizontalFlip + prob: 0.2 + - type: RandomRotate + prob: 0.2 + + diff --git a/configs/swingait/swingait3D_B1442C2_Gait3D.yaml b/configs/swingait/swingait3D_B1442C2_Gait3D.yaml new file mode 100644 index 0000000..0d341a7 --- /dev/null +++ b/configs/swingait/swingait3D_B1442C2_Gait3D.yaml @@ -0,0 +1,99 @@ +data_cfg: + dataset_name: Gait3D + dataset_root: your_path + dataset_partition: ./datasets/Gait3D/Gait3D.json + num_workers: 1 + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: Gait3D + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 80000 + save_name: SwinGait3D_B1442_C2 + sampler: + batch_shuffle: false + batch_size: 4 + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + eval_func: evaluate_Gait3D + metric: euc # cos + transform: + - type: BaseSilTransform + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + - loss_term_weight: 1.0 + scale: 16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + +model_cfg: + model: SwinGait + Backbone: + mode: p3d + in_channels: 1 + layers: + - 1 + - 4 + - 4 + - 2 + channels: + - 64 + - 128 + bin_num: + - 15 + SeparateBNNecks: + in_channels: 256 + class_num: 3000 + parts_num: 16 + +optimizer_cfg: + lr: 0.0003 + solver: AdamW + weight_decay: 0.02 + +scheduler_cfg: + gamma: 0.1 + milestones: # Learning Rate Reduction at each milestones + - 100000000 + scheduler: MultiStepLR + +trainer_cfg: + enable_float16: true # half_percesion float for memory reduction and speedup + fix_BN: false + log_iter: 100 + with_test: false + optimizer_reset: True + restore_ckpt_strict: false + restore_hint: ./output/Gait3D/DeepGaitV2/DeepGaitV2_B1441_C2_P3D/checkpoints/DeepGaitV2_B1441_C2_P3D-60000.pt + save_iter: 20000 + save_name: SwinGait3D_B1442_C2 + sync_BN: true + T_max_iter: 60000 + total_iter: 80000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 30 # fixed frames number for training + frames_skip_num: 4 + sample_type: fixed_ordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + transform: + - type: Compose + trf_cfg: + - type: RandomPerspective + prob: 0.2 + - type: BaseSilTransform + - type: RandomHorizontalFlip + prob: 0.2 + - type: RandomRotate + prob: 0.2 + + diff --git a/opengait/modeling/models/swingait.py b/opengait/modeling/models/swingait.py new file mode 100644 index 0000000..868e255 --- /dev/null +++ b/opengait/modeling/models/swingait.py @@ -0,0 +1,945 @@ +import torch +import torch.nn as nn +from ..base_model import BaseModel +from ..modules import HorizontalPoolingPyramid, PackSequenceWrapper, SeparateFCs, SeparateBNNecks, SetBlockWrapper, ParallelBN1d + +# ******* Copy from https://github.com/haofanwang/video-swin-transformer-pytorch/blob/main/video_swin_transformer.py ******* +from functools import reduce, lru_cache +from operator import mul +from einops import rearrange + +import torch.nn.functional as F +class Mlp(nn.Module): + """ Multilayer perceptron.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, D, H, W, C) + window_size (tuple[int]): window size + Returns: + windows: (B*num_windows, window_size*window_size, C) + """ + B, D, H, W, C = x.shape + x = x.view(B, D // window_size[0], window_size[0], H // window_size[1], window_size[1], W // window_size[2], window_size[2], C) + windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), C) + return windows + + +def window_reverse(windows, window_size, B, D, H, W): + """ + Args: + windows: (B*num_windows, window_size, window_size, C) + window_size (tuple[int]): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, D, H, W, C) + """ + x = windows.view(B, D // window_size[0], H // window_size[1], W // window_size[2], window_size[0], window_size[1], window_size[2], -1) + x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1) + return x + + +def get_window_size(x_size, window_size, shift_size=None): + use_window_size = list(window_size) + if shift_size is not None: + use_shift_size = list(shift_size) + for i in range(len(x_size)): + if x_size[i] <= window_size[i]: + use_window_size[i] = x_size[i] + if shift_size is not None: + use_shift_size[i] = 0 + + if shift_size is None: + return tuple(use_window_size) + else: + return tuple(use_window_size), tuple(use_shift_size) + + +from torch.nn.init import _calculate_fan_in_and_fan_out + + +import math +import warnings +# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/weight_init.py +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + +# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/weight_init.py +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + # type: (Tensor, float, float, float, float) -> Tensor + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are + applied while sampling the normal with mean/std applied, therefore a, b args + should be adjusted to match the range of mean, std args. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + with torch.no_grad(): + return _trunc_normal_(tensor, mean, std, a, b) + +class WindowAttention3D(nn.Module): + """ Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The temporal length, height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wd, Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1) * (2 * window_size[2] - 1), num_heads)) # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_d = torch.arange(self.window_size[0]) + coords_h = torch.arange(self.window_size[1]) + coords_w = torch.arange(self.window_size[2]) + coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w)) # 3, Wd, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 3, Wd*Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 3, Wd*Wh*Ww, Wd*Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wd*Wh*Ww, Wd*Wh*Ww, 3 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 2] += self.window_size[2] - 1 + + relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1) + relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1) + relative_position_index = relative_coords.sum(-1) # Wd*Wh*Ww, Wd*Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ Forward function. + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, N, N) or None + """ + B_, N, C = x.shape + qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # B_, nH, N, C + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index[:N, :N].reshape(-1)].reshape( + N, N, -1) # Wd*Wh*Ww,Wd*Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wd*Wh*Ww, Wd*Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) # B_, nH, N, N + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + +# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/drop.py +def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + +# Copy from https://github.com/rwightman/pytorch-image-models/blob/8ff45e41f7a6aba4d5fdadee7dc3b7f2733df045/timm/models/layers/drop.py +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f'drop_prob={round(self.drop_prob,3):0.3f}' + +class SwinTransformerBlock3D(nn.Module): + """ Swin Transformer Block. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (tuple[int]): Window size. + shift_size (tuple[int]): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, dim, num_heads, window_size=(2,7,7), shift_size=(0,0,0), + mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_checkpoint=False): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + self.use_checkpoint=use_checkpoint + + assert 0 <= self.shift_size[0] < self.window_size[0], "shift_size must in 0-window_size" + assert 0 <= self.shift_size[1] < self.window_size[1], "shift_size must in 0-window_size" + assert 0 <= self.shift_size[2] < self.window_size[2], "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention3D( + dim, window_size=self.window_size, num_heads=num_heads, + qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward_part1(self, x, mask_matrix): + B, D, H, W, C = x.shape + window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size) + + x = self.norm1(x) + # pad feature maps to multiples of window size + pad_l = pad_t = pad_d0 = 0 + pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0] + pad_b = (window_size[1] - H % window_size[1]) % window_size[1] + pad_r = (window_size[2] - W % window_size[2]) % window_size[2] + x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1)) + _, Dp, Hp, Wp, _ = x.shape + # cyclic shift + if any(i > 0 for i in shift_size): + shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3)) + attn_mask = mask_matrix + else: + shifted_x = x + attn_mask = None + # partition windows + x_windows = window_partition(shifted_x, window_size) # B*nW, Wd*Wh*Ww, C + # W-MSA/SW-MSA + attn_windows = self.attn(x_windows, mask=attn_mask) # B*nW, Wd*Wh*Ww, C + # merge windows + attn_windows = attn_windows.view(-1, *(window_size+(C,))) + shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp) # B D' H' W' C + # reverse cyclic shift + if any(i > 0 for i in shift_size): + x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3)) + else: + x = shifted_x + + if pad_d1 >0 or pad_r > 0 or pad_b > 0: + x = x[:, :D, :H, :W, :].contiguous() + return x + + def forward_part2(self, x): + return self.drop_path(self.mlp(self.norm2(x))) + + def forward(self, x, mask_matrix): + """ Forward function. + Args: + x: Input feature, tensor size (B, D, H, W, C). + mask_matrix: Attention mask for cyclic shift. + """ + + shortcut = x + if self.use_checkpoint: + x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix) + else: + x = self.forward_part1(x, mask_matrix) + x = shortcut + self.drop_path(x) + + if self.use_checkpoint: + x = x + checkpoint.checkpoint(self.forward_part2, x) + else: + x = x + self.forward_part2(x) + + return x + + +class PatchMerging(nn.Module): + """ Patch Merging Layer + Args: + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + def __init__(self, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ Forward function. + Args: + x: Input feature, tensor size (B, D, H, W, C). + """ + B, D, H, W, C = x.shape + + # padding + pad_input = (H % 2 == 1) or (W % 2 == 1) + if pad_input: + x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) + + x0 = x[:, :, 0::2, 0::2, :] # B D H/2 W/2 C + x1 = x[:, :, 1::2, 0::2, :] # B D H/2 W/2 C + x2 = x[:, :, 0::2, 1::2, :] # B D H/2 W/2 C + x3 = x[:, :, 1::2, 1::2, :] # B D H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B D H/2 W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + +# cache each stage results +@lru_cache() +def compute_mask(D, H, W, window_size, shift_size, device): + img_mask = torch.zeros((1, D, H, W, 1), device=device) # 1 Dp Hp Wp 1 + cnt = 0 + for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0],None): + for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1],None): + for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2],None): + img_mask[:, d, h, w, :] = cnt + cnt += 1 + mask_windows = window_partition(img_mask, window_size) # nW, ws[0]*ws[1]*ws[2], 1 + mask_windows = mask_windows.squeeze(-1) # nW, ws[0]*ws[1]*ws[2] + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + return attn_mask + +import numpy as np +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of feature channels + depth (int): Depths of this stage. + num_heads (int): Number of attention head. + window_size (tuple[int]): Local window size. Default: (1,7,7). + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + """ + + def __init__(self, + dim, + depth, + num_heads, + window_size=(1,7,7), + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False): + super().__init__() + self.window_size = window_size + self.shift_size = tuple(i // 2 for i in window_size) + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock3D( + dim=dim, + num_heads=num_heads, + window_size=window_size, + shift_size=(0,0,0) if (i % 2 == 0) else self.shift_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer, + use_checkpoint=use_checkpoint, + ) + for i in range(depth)] + ) + + self.downsample = downsample + if self.downsample == False: + self.downsample = lambda x: x + else: + if self.downsample is not None: + self.downsample = downsample(dim=dim, norm_layer=norm_layer) + else: + self.downsample = nn.Sequential( + norm_layer(dim), + nn.Linear(dim, 2 * dim, bias=False) + ) + + def forward(self, x): + """ Forward function. + Args: + x: Input feature, tensor size (B, C, D, H, W). + """ + # calculate attention mask for SW-MSA + B, C, D, H, W = x.shape + window_size, shift_size = get_window_size((D,H,W), self.window_size, self.shift_size) + x = rearrange(x, 'b c d h w -> b d h w c') + Dp = int(np.ceil(D / window_size[0])) * window_size[0] + Hp = int(np.ceil(H / window_size[1])) * window_size[1] + Wp = int(np.ceil(W / window_size[2])) * window_size[2] + attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device) + for blk in self.blocks: + x = blk(x, attn_mask) + x = x.view(B, D, H, W, -1) + + if self.downsample is not None: + x = self.downsample(x) + x = rearrange(x, 'b d h w c -> b c d h w') + return x + + +class PatchEmbed3D(nn.Module): + """ Video to Patch Embedding. + Args: + patch_size (int): Patch token size. Default: (2,4,4). + in_chans (int): Number of input video channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + def __init__(self, patch_size=(2,4,4), in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + self.patch_size = patch_size + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + """Forward function.""" + # padding + _, _, D, H, W = x.size() + if W % self.patch_size[2] != 0: + x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2])) + if H % self.patch_size[1] != 0: + x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1])) + if D % self.patch_size[0] != 0: + x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0])) + + x = self.proj(x) # B C D Wh Ww + if self.norm is not None: + D, Wh, Ww = x.size(2), x.size(3), x.size(4) + x = x.flatten(2).transpose(1, 2) + x = self.norm(x) + x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww) + + return x + +class SwinTransformer3D(nn.Module): + """ Swin Transformer backbone. + A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - + https://arxiv.org/pdf/2103.14030 + Args: + patch_size (int | tuple(int)): Patch size. Default: (4,4,4). + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + depths (tuple[int]): Depths of each Swin Transformer stage. + num_heads (tuple[int]): Number of attention head of each stage. + window_size (int): Window size. Default: 7. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. + drop_rate (float): Dropout rate. + attn_drop_rate (float): Attention dropout rate. Default: 0. + drop_path_rate (float): Stochastic depth rate. Default: 0.2. + norm_layer: Normalization layer. Default: nn.LayerNorm. + patch_norm (bool): If True, add normalization after patch embedding. Default: False. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + """ + + def __init__(self, + pretrained=None, + pretrained2d=True, + patch_size=(4,4,4), + in_chans=3, + embed_dim=96, + depths=[2, 2, 6, 2], + num_heads=[3, 6, 12, 24], + window_size=(2,7,7), + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + norm_layer=nn.LayerNorm, + patch_norm=False, + frozen_stages=-1, + use_checkpoint=False, + downsample=[1, 2, 2, 1]): + super().__init__() + + self.pretrained = pretrained + self.pretrained2d = pretrained2d + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.patch_norm = patch_norm + self.frozen_stages = frozen_stages + self.window_size = window_size + self.patch_size = patch_size + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed3D( + patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build layers + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + if downsample[i_layer]== 2: + dfunc = PatchMerging + elif downsample[i_layer] == 1: + dfunc = None + elif downsample[i_layer] == 0: + dfunc = False + else: + raise ValueError('xxx') + + layer = BasicLayer( + dim=int(embed_dim * 2**i_layer), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], + norm_layer=norm_layer, + downsample=dfunc, + use_checkpoint=use_checkpoint) + + self.layers.append(layer) + + # self.num_features = int(embed_dim * 2**self.num_layers) + self.num_features = 512 + + # add a norm layer for each output + self.norm = norm_layer(self.num_features) + + self._freeze_stages() + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + if self.frozen_stages >= 1: + self.pos_drop.eval() + for i in range(0, self.frozen_stages): + m = self.layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def inflate_weights(self, logger): + """Inflate the swin2d parameters to swin3d. + The differences between swin3d and swin2d mainly lie in an extra + axis. To utilize the pretrained parameters in 2d model, + the weight of swin2d models should be inflated to fit in the shapes of + the 3d counterpart. + Args: + logger (logging.Logger): The logger used to print + debugging infomation. + """ + checkpoint = torch.load(self.pretrained, map_location='cpu') + state_dict = checkpoint['model'] + + # delete relative_position_index since we always re-init it + relative_position_index_keys = [k for k in state_dict.keys() if "relative_position_index" in k] + for k in relative_position_index_keys: + del state_dict[k] + + # delete attn_mask since we always re-init it + attn_mask_keys = [k for k in state_dict.keys() if "attn_mask" in k] + for k in attn_mask_keys: + del state_dict[k] + + state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).repeat(1,1,self.patch_size[0],1,1) / self.patch_size[0] + + # bicubic interpolate relative_position_bias_table if not match + relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k] + for k in relative_position_bias_table_keys: + relative_position_bias_table_pretrained = state_dict[k] + relative_position_bias_table_current = self.state_dict()[k] + L1, nH1 = relative_position_bias_table_pretrained.size() + L2, nH2 = relative_position_bias_table_current.size() + L2 = (2*self.window_size[1]-1) * (2*self.window_size[2]-1) + wd = self.window_size[0] + if nH1 != nH2: + logger.warning(f"Error in loading {k}, passing") + else: + if L1 != L2: + S1 = int(L1 ** 0.5) + relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate( + relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(2*self.window_size[1]-1, 2*self.window_size[2]-1), + mode='bicubic') + relative_position_bias_table_pretrained = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0) + state_dict[k] = relative_position_bias_table_pretrained.repeat(2*wd-1,1) + + msg = self.load_state_dict(state_dict, strict=False) + logger.info(msg) + logger.info(f"=> loaded successfully '{self.pretrained}'") + del checkpoint + torch.cuda.empty_cache() + + def init_weights(self, pretrained=None): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + if pretrained: + self.pretrained = pretrained + if isinstance(self.pretrained, str): + self.apply(_init_weights) + logger = get_root_logger() + logger.info(f'load model from: {self.pretrained}') + + if self.pretrained2d: + # Inflate 2D model into 3D model. + self.inflate_weights(logger) + else: + # Directly load 3D model. + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + elif self.pretrained is None: + self.apply(_init_weights) + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + """Forward function.""" + x = self.patch_embed(x) + + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x.contiguous()) + + x = rearrange(x, 'n c d h w -> n d h w c') + x = self.norm(x) + x = rearrange(x, 'n d h w c -> n c d h w') + + return x + + def train(self, mode=True): + """Convert the model into training mode while keep layers freezed.""" + super(SwinTransformer3D, self).train(mode) + self._freeze_stages() + +# ******* Copy from https://github.com/haofanwang/video-swin-transformer-pytorch/blob/main/video_swin_transformer.py ******* + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + +# import copy + +from ..modules import BasicBlock2D, BasicBlockP3D + +import torch.optim as optim +import os.path as osp +from collections import OrderedDict +from utils import get_valid_args, get_attr_from + +class SwinGait(BaseModel): + def __init__(self, cfgs, training): + self.T_max_iter = cfgs['trainer_cfg']['T_max_iter'] + super(SwinGait, self).__init__(cfgs, training=training) + + def build_network(self, model_cfg): + channels = model_cfg['Backbone']['channels'] + layers = model_cfg['Backbone']['layers'] + in_c = model_cfg['Backbone']['in_channels'] + + self.inplanes = channels[0] + self.layer0 = SetBlockWrapper(nn.Sequential( + conv3x3(in_c, self.inplanes, 1), + nn.BatchNorm2d(self.inplanes), + nn.ReLU(inplace=True) + )) + self.layer1 = SetBlockWrapper(self.make_layer(BasicBlock2D, channels[0], stride=[1, 1], blocks_num=layers[0], mode='2d')) + self.layer2 = self.make_layer(BasicBlockP3D, channels[1], stride=[2, 2], blocks_num=layers[1], mode='p3d') + + self.ulayer = SetBlockWrapper(nn.UpsamplingBilinear2d(size=(30, 20))) + + self.transformer = SwinTransformer3D( + patch_size = [1, 2, 2], + in_chans = channels[1], + embed_dim = 256, + depths = [layers[2], layers[3]], + num_heads = [16, 32], + window_size = [3, 3, 5], + downsample = [1, 0], + drop_path_rate = 0.1, + patch_norm = True, + ) + + self.FCs = SeparateFCs(model_cfg['SeparateBNNecks']['parts_num'], in_channels=512, out_channels=256) + self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks']) + self.TP = PackSequenceWrapper(torch.max) + self.HPP = HorizontalPoolingPyramid(bin_num=model_cfg['bin_num']) + + def get_optimizer(self, optimizer_cfg): + self.msg_mgr.log_info(optimizer_cfg) + optimizer = get_attr_from([optim], optimizer_cfg['solver']) + valid_arg = get_valid_args(optimizer, optimizer_cfg, ['solver']) + + transformer_no_decay = ['patch_embed', 'norm', 'relative_position_bias_table'] + transformer_params = list(self.transformer.named_parameters()) + params_list = [ + {'params': [p for n, p in transformer_params if any(nd in n for nd in transformer_no_decay)], 'lr': optimizer_cfg['lr'], 'weight_decay': 0.}, + {'params': [p for n, p in transformer_params if not any(nd in n for nd in transformer_no_decay)], 'lr': optimizer_cfg['lr'], 'weight_decay': optimizer_cfg['weight_decay']}, + {'params': self.FCs.parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']}, + {'params': self.BNNecks.parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']}, + ] + for i in range(5): + if hasattr(self, 'layer%d'%i): + params_list.append( + {'params': getattr(self, 'layer%d'%i).parameters(), 'lr': optimizer_cfg['lr'] * 0.1, 'weight_decay': optimizer_cfg['weight_decay']} + ) + + optimizer = optimizer(params_list) + return optimizer + + def init_parameters(self): + for m in self.modules(): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + elif isinstance(m, (nn.Conv3d, nn.Conv2d, nn.Conv1d)): + nn.init.xavier_uniform_(m.weight.data) + if m.bias is not None: + nn.init.constant_(m.bias.data, 0.0) + elif isinstance(m, (nn.BatchNorm3d, nn.BatchNorm2d, nn.BatchNorm1d)): + if m.affine: + nn.init.normal_(m.weight.data, 1.0, 0.02) + nn.init.constant_(m.bias.data, 0.0) + + def make_layer(self, block, planes, stride, blocks_num, mode='2d'): + + if max(stride) > 1 or self.inplanes != planes * block.expansion: + if mode == '3d': + downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=stride, padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion)) + elif mode == '2d': + downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride=stride), nn.BatchNorm2d(planes * block.expansion)) + elif mode == 'p3d': + downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=[1, 1, 1], stride=[1, *stride], padding=[0, 0, 0], bias=False), nn.BatchNorm3d(planes * block.expansion)) + else: + raise TypeError('xxx') + else: + downsample = lambda x: x + + layers = [block(self.inplanes, planes, stride=stride, downsample=downsample)] + self.inplanes = planes * block.expansion + s = [1, 1] if mode in ['2d', 'p3d'] else [1, 1, 1] + for i in range(1, blocks_num): + layers.append( + block(self.inplanes, planes, stride=s) + ) + return nn.Sequential(*layers) + + def forward(self, inputs): + if self.training: + adjust_learning_rate(self.optimizer, self.iteration, T_max_iter=self.T_max_iter) + ipts, labs, _, _, seqL = inputs + + sils = ipts[0].unsqueeze(1) + + del ipts + + out0 = self.layer0(sils) + out1 = self.layer1(out0) + out2 = self.layer2(out1) # [n, c, s, h, w] + out2 = self.ulayer(out2) + out4 = self.transformer(out2) # [n, 768, s/4, 4, 3] + + # Temporal Pooling, TP + outs = self.TP(out4, seqL, options={"dim": 2})[0] # [n, c, h, w] + # Horizontal Pooling Matching, HPM + feat = self.HPP(outs) # [n, c, p] + + feat = torch.cat([feat, feat[:, :, -1].clone().detach().unsqueeze(-1)], dim=-1) + embed_1 = self.FCs(feat) # [n, c, p] + embed_2, logits = self.BNNecks(embed_1) # [n, c, p] + embed_1 = embed_1.contiguous()[:, :, :-1] # [n, p, c] + embed_2 = embed_2.contiguous()[:, :, :-1] # [n, p, c] + logits = logits.contiguous()[:, :, :-1] # [n, p, c] + + embed = embed_1 + + retval = { + 'training_feat': { + 'triplet': {'embeddings': embed_1, 'labels': labs}, + 'softmax': {'logits': logits, 'labels': labs} + }, + 'visual_summary': { + 'image/sils': rearrange(sils,'n c s h w -> (n s) c h w') + }, + 'inference_feat': { + 'embeddings': embed + } + } + return retval + +import math +def adjust_learning_rate(optimizer, iteration, iteration_per_epoch=1000, T_max_iter=10000, min_lr=1e-6): + """Decay the learning rate based on schedule""" + if iteration < T_max_iter: + if iteration % iteration_per_epoch == 0 : + alpha = 0.5 * (1. + math.cos(math.pi * iteration / T_max_iter)) + for param_group in optimizer.param_groups: + param_group['lr'] = max(param_group['initial_lr'] * alpha, min_lr) + else: + pass + elif iteration == T_max_iter: + for param_group in optimizer.param_groups: + param_group['lr'] = min_lr + else: + pass