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How to use num_items method in localstack
Best Python code snippet using localstack_python
smt_mtz.py
Source:smt_mtz.py 
1"""2CVRP using Z3 and SMT3"""4import sys5import numpy as np6from scipy.spatial.distance import cityblock7import networkx as nx8import matplotlib.pyplot as plt9import random10from z3 import Int,Bool,Solver,Optimize,PbLe,PbEq,Implies,And,Not,Or,If,Sum,sat,set_option1112# Save the solution if any 13def on_model(model):14 out = open('../out/out'+sys.argv[1][1:][3:]+'.txt', 'w')15 print("Solution found")16 out.write("Solution found")17 out.write("\n")18 tot = Int('tot')19 print("Total length of the routes: "+ str(model[tot]))20 out.write("Total length of the routes: "+ str(model[tot]))21 out.write("\n")22 track = [[[Bool(f"x_{i}_{j}_{k}") for k in range(num_courier)] for j in range(num_item+1)] for i in range(num_item+1)]23 t = [[[ model.evaluate(track[i][j][k]) for k in range(num_courier)]for j in range(num_item+1)]for i in range(num_item+1)]24 edges=[[] for c in range(num_courier)]25 for c in range(num_courier):26 print("Route courier "+str(c+1)+":")27 out.write("Route courier "+str(c+1)+":")28 out.write("\n")29 active_arcs={}30 nodes=list(range(num_item))31 nodes.insert(0,num_item)32 for i in nodes:33 for j in nodes:34 if t[i][j][c]:35 active_arcs[i]=j36 finish = False37 i = num_item38 route=str(0)+'->'39 while not finish :40 j=active_arcs.get(i)41 edges[c].append((i,j))42 i=j43 if(j==num_item):44 route+=str(0)45 finish=True46 else:47 route+= str(j+1)+'->'48 print(route)49 out.write(route)50 out.write("\n")51 print()52 out.close()5354 # Plot the tour of each vehicle55 fig, ax = plt.subplots()56 G=nx.Graph(name="route_smt")57 for c in range(num_courier):58 G.add_nodes_from(edges[c][:][0])59 G.add_edges_from(edges[c])60 coordinates = np.array([ [x,y] for x,y in zip(xc, yc)])61 nodes = np.arange(0,num_item+1)62 npos = dict(zip(nodes, coordinates))63 nx.draw_networkx_nodes(G,pos=npos, node_size=9)64 colors = [ "#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(edges))]65 linewidth = 266 plt.plot(xc[-1], yc[-1], c='r', marker='s')67 for ctr, edgelist in enumerate(edges):68 nx.draw_networkx_edges(G,pos=npos,edgelist=edgelist,edge_color = colors[ctr], width=linewidth)69 plt.axis('on')70 ax.tick_params(left=True, bottom=True, labelleft=True, labelbottom=True)71 plt.savefig('../plots/route_smt_'+sys.argv[1]+'.png')72 7374def courier_scheduling_problem_MTZ(num_items, num_couriers, items_weights, courier_loads, dist):75 track = [[[Bool(f"x_{i}_{j}_{k}") for k in range(num_couriers)] for j in range(num_items+1)] for i in range(num_items+1)]76 u = [Int(f"u_{j}")for j in range(num_items)]77 tot = Int("tot")78 s = Optimize()79 s.set_on_model(on_model)80 s.set("maxsat_engine",'core_maxsat')81 s.set("timeout",300000)82 #s = Solver()8384 # Main diagonal equal to 0 85 for i in range(num_items+1):86 #s.add(And([Not(track[i][i][c]) for c in range(num_couriers)]))87 s.add(PbEq([88 (track[i][i][c], 1) for c in range(num_couriers)89 ], 0))90 9192 # Each node visited only once93 for j in range(num_items):94 #s.add(And(exactly_one([track[i][j][c] for c in range(num_courier) for i in range(num_items+1)]), exactly_one([track[j][i][c] for c in range(num_courier) for i in range(num_items+1)])))95 s.add(And(PbEq([96 (track[i][j][c],1) for c in range(num_couriers) for i in range(num_items+1)97 ],1),98 PbEq([99 (track[j][i][c],1) for c in range(num_couriers) for i in range(num_items+1)100 ],1)))101102 103 # Each courier can go back and depart from depot at maximum one time104 for c in range(num_couriers):105 #s.add(And(exactly_one([track[num_items][j][c] for j in range(num_items)]), exactly_one([track[j][num_items][c] for j in range(num_items)])))106 s.add(And(PbEq([(track[num_items][j][c],1) for j in range(num_items)],1),107 PbEq([(track[j][num_items][c],1) for j in range(num_items)],1)))108109110 # Constraint on the load of each courier111 for c in range(num_couriers):112 #s.add(courier_loads[c]>= Sum([If(track[i][j][c],items_weights[i],0) for i in range(num_items) for j in range(num_items+1)]))113 s.add(PbLe([114 (track[i][j][c], items_weights[i]) for i in range(num_items) for j in range(num_items+1)115 ], courier_loads[c]))116117 # N arcs in = n arcs out118 for c in range(num_couriers):119 for j in range(num_items+1):120 # orig121 s.add(Sum([If(track[i][j][c],1,0) for i in range(num_items+1)])==Sum([If(track[j][i][c],1,0) for i in range(num_items+1)]))122 #s.add(PbEq([(track[i][j][c],1) for i in range(num_items+1)],Sum([(track[j][i][c],1) for i in range(num_items+1)])))123124125 # Miller-Tucker-Zemlin formulation126 for c in range(num_courier):127 for i in range(num_items):128 for j in range(num_items):129 s.add(u[i] + If(track[i][j][c],1,0) <= u[j] + num_items*(1-If(track[i][j][c],1,0)))130 s.add(u[i] > 0)131 132133 # Constraint to obtain the total length of the routes 134 s.add(tot==Sum([If(track[i][j][c],dist[i][j],0) for i in range(num_items+1) for j in range(num_items+1) for c in range(num_couriers)]))135 s.minimize(tot)136137 print("Model loaded, starting to solve the "+sys.argv[1]+".")138139 if s.check() == sat:140 m = s.model()141 t = m.evaluate(tot)142 r = [[[m.evaluate(track[i][j][k]) for k in range(num_couriers)]for j in range(num_items+1)]for i in range(num_items+1)]143 for c in range(num_couriers):144 print('Courier', c)145 nodes=list(range(num_items))146 nodes.insert(0,num_items)147 for i in nodes:148 for j in nodes:149 if r[i][j][c] == True:150 print(i,'->',j)151 print("satttttttttttttttttttttttttt")152 print(r)153 return t154 else:155 print("unsat")156157158159def main(argv):160 # Instantiate variables from file161 file= open('./MCP_Instances/'+argv)162 lines=[]163 for line in file:164 lines.append(line)165 file.close()166 global num_courier167 num_courier=int(lines[0].rstrip('\n'))168 global num_item169 num_item=int(lines[1].rstrip('\n'))170 load=list(map(int, lines[2].rstrip('\n').split()))171 weight=list(map(int, lines[3].rstrip('\n').split()))172 global xc173 xc=list(map(int, lines[4].rstrip('\n').split()))174 global yc175 yc=list(map(int, lines[5].rstrip('\n').split()))176 load = sorted(load, reverse=True)177 for i in range(len(load)):178 if sum(load[0:i])>= sum(weight):179 load1 = load[0:i]180 load=load1181 num_courier = i182 break183 dist=np.zeros((len(xc),len(xc)))184 for i in range(len(xc)):185 for j in range(len(xc)):186 dist[i,j] = int(cityblock([xc[i],yc[i]],[xc[j],yc[j]]))187 dist=dist.astype(int)188 dist=dist.tolist()189 # Call to the Z3 SMT MAXSAT solver190 tot = courier_scheduling_problem_MTZ(num_item, num_courier, weight, load, dist)191192 """Alternative model that uses lazy constraints: to use it comment the lines above and uncomment the lines below"""193 # tot,best_track = courier_scheduling_problem_MTZ_lazy(num_item, num_courier, weight, load, dist)194 # out = open('../out/out'+sys.argv[1][1:][3:]+'.txt', 'w')195 # print("Solution found")196 # out.write("Solution found")197 # out.write("\n")198 # print("Total length of the routes: "+ str(tot))199 # out.write("Total length of the routes: "+ str(tot))200 # out.write("\n")201 # edges=[[] for c in range(num_courier)]202 # for c in range(num_courier):203 # print('Courier', c+1)204 # active_arcs={}205 # nodes=list(range(num_item))206 # nodes.insert(0,num_item)207 # for i in nodes:208 # for j in nodes:209 # if best_track[i][j][c]:210 # active_arcs[i]=j211 # finish = False212 # i = num_item213 # route=str(i)+'->'214 # while not finish :215 # j=active_arcs.get(i)216 # edges[c].append((i,j))217 # i=j218 # if(j==num_item):219 # route+=str(num_item)220 # finish=True221 # else:222 # route+= str(j)+'->'223 # print(route)224 # out.write(route)225 # out.write("\n")226 # print()227 # out.close()228 # # plot the tour of each vehicle229 # fig, ax = plt.subplots()230 # G=nx.Graph(name="route_smt")231 # for c in range(num_courier):232 # G.add_nodes_from(edges[c][:][0])233 # G.add_edges_from(edges[c])234 # coordinates = np.array([ [x,y] for x,y in zip(xc, yc)])235 # nodes = np.arange(0,num_item+1)236 # npos = dict(zip(nodes, coordinates))237 # nx.draw_networkx_nodes(G,pos=npos, node_size=35)238 # colors = [ "#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(edges))]239 # linewidths = 2240 # for ctr, edgelist in enumerate(edges):241 # nx.draw_networkx_edges(G,pos=npos,edgelist=edgelist,edge_color = colors[ctr], width=linewidth)242 # plt.axis('on')243 # plt.grid(visible=True)244 # ax.tick_params(left=True, bottom=True, labelleft=True, labelbottom=True)245 # plt.savefig('../plots/route_smt_lazy_'+sys.argv[1]+'.png')246247if __name__ == "__main__":248 main(sys.argv[1])249250251def courier_scheduling_problem_MTZ_lazy(num_items, num_couriers, items_weights, courier_loads, dist):252 track = [[[Bool(f"x_{i}_{j}_{k}") for k in range(num_courier)] for j in range(num_items+1)] for i in range(num_items+1)]253 u = [Int(f"u_{j}")for j in range(num_items)]254 tot = Int("tot")255 s = Solver()256 s.set("timeout",300000)257258 # Tot constraint259 s.add(tot==Sum([If(track[i][j][c],dist[i][j],0) for i in range(num_items+1) for j in range(num_items+1) for c in range(num_couriers)]))260 set_option("verbose", 2)261262 # Main diagonal equal to 0 263 for i in range(num_items+1):264 #s.add(And([Not(track[i][i][c]) for c in range(num_couriers)]))265 s.add(PbEq([266 (track[i][i][c], 1) for c in range(num_couriers)267 ], 0))268 269270 # Each node visited only once271 for j in range(num_items):272 # orig273 #s.add(And(exactly_one([track[i][j][c] for c in range(num_courier) for i in range(num_items+1)]), exactly_one([track[j][i][c] for c in range(num_courier) for i in range(num_items+1)])))274 s.add(And(PbEq([275 (track[i][j][c],1) for c in range(num_courier) for i in range(num_items+1)276 ],1),277 PbEq([278 (track[j][i][c],1) for c in range(num_courier) for i in range(num_items+1)279 ],1)))280281 282 # # Each courier can go back and depart from depot at maximum one time283 for c in range(num_courier):284 s.add(And(PbEq([(track[num_items][j][c],1) for j in range(num_items)],1), PbEq([(track[j][num_items][c],1) for j in range(num_items)],1)))285286 # Constraint on the load of each courier287 for c in range(num_courier):288 #s.add(courier_loads[c]>= Sum([If(track[i][j][c],items_weights[i],0) for i in range(num_items) for j in range(num_items+1)]))289 s.add(PbLe([290 (track[i][j][c], items_weights[i]) for i in range(num_items) for j in range(num_items+1)291 ], courier_loads[c]))292293 # N arcs in = n arcs out294 for c in range(num_courier):295 for j in range(num_items):296 # orig297 s.add(Sum([If(track[i][j][c],1,0) for i in range(num_items+1)])==Sum([If(track[j][i][c],1,0) for i in range(num_items+1)]))298 #s.add(PbEq([(track[i][j][c],1) for i in range(num_items+1)],1))299300 # Constraint to eliminate the i->j->i possibility301 for c in range(num_courier):302 for i in range(num_items):303 for j in range(num_items):304 s.add(Implies(track[i][j][c], Not(track[j][i][c])))305306 # Constraint to compute the sum of our tour307 best = 1000000308 best_track=[[[False]]]309 for i in range(1000):310 if s.check() == sat:311 m = s.model()312 t = m.evaluate(tot)313 routes = [[ [ m.evaluate(track[i][j][k]) for k in range(num_couriers)] for j in range(num_items+1)] for i in range(num_items+1)]314 subtour=False315 tours =[[] for i in range(num_couriers) ]316 for c in range(num_courier):317 list1 = list(range(num_items))318 list1.insert(0,num_items)319 for i in list1:320 for j in list1:321 if routes[i][j][c] == True:322 tours[c].append((i,j))323 for c in range(num_couriers):324 if(len(tours[c])>0):325 tmp=[tours[c][0]]326 i=1327 while i < len(tours[c]):328 if(tmp[-1][1]==tours[c][i][0]):329 if tours[c][i] not in tmp:330 tmp.append(tours[c][i])331 i=0332 i+=1333 if(len(tmp)!=len(tours[c])):334 subtour=True335 sub=[i[0] for i in [j for j in tmp]]336 for i in sub:337 for k in range(num_items):338 if(i!=num_items):339 s.add(u[i] + If(track[i][k][c],1,0) <= u[k] + num_items*(1-If(track[i][k][c],1,0)))340 if (not subtour) and (t.as_long() < best) :341 s.add(tot < t.as_long())342 best = t.as_long()343 best_track=routes344 else:345 print("unsat")
...
metric_utils.py
Source:metric_utils.py
1# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.2#3# NVIDIA CORPORATION and its licensors retain all intellectual property4# and proprietary rights in and to this software, related documentation5# and any modifications thereto. Any use, reproduction, disclosure or6# distribution of this software and related documentation without an express7# license agreement from NVIDIA CORPORATION is strictly prohibited.8"""Miscellaneous utilities used internally by the quality metrics."""9import os10import time11import hashlib12import pickle13import copy14import uuid15import numpy as np16import torch17import dnnlib18#----------------------------------------------------------------------------19class MetricOptions:20 def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True):21 assert 0 <= rank < num_gpus22 self.G = G23 self.G_kwargs = dnnlib.EasyDict(G_kwargs)24 self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs)25 self.num_gpus = num_gpus26 self.rank = rank27 self.device = device if device is not None else torch.device('cuda', rank)28 self.progress = progress.sub() if progress is not None and rank == 0 else ProgressMonitor()29 self.cache = cache30#----------------------------------------------------------------------------31_feature_detector_cache = dict()32def get_feature_detector_name(url):33 return os.path.splitext(url.split('/')[-1])[0]34def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False):35 assert 0 <= rank < num_gpus36 key = (url, device)37 if key not in _feature_detector_cache:38 is_leader = (rank == 0)39 if not is_leader and num_gpus > 1:40 torch.distributed.barrier() # leader goes first41 with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f:42 _feature_detector_cache[key] = pickle.load(f).to(device)43 if is_leader and num_gpus > 1:44 torch.distributed.barrier() # others follow45 return _feature_detector_cache[key]46#----------------------------------------------------------------------------47def iterate_random_labels(opts, batch_size):48 if opts.G.c_dim == 0:49 c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device)50 while True:51 yield c52 else:53 dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)54 while True:55 c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)]56 c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)57 yield c58#----------------------------------------------------------------------------59class FeatureStats:60 def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None):61 self.capture_all = capture_all62 self.capture_mean_cov = capture_mean_cov63 self.max_items = max_items64 self.num_items = 065 self.num_features = None66 self.all_features = None67 self.raw_mean = None68 self.raw_cov = None69 def set_num_features(self, num_features):70 if self.num_features is not None:71 assert num_features == self.num_features72 else:73 self.num_features = num_features74 self.all_features = []75 self.raw_mean = np.zeros([num_features], dtype=np.float64)76 self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64)77 def is_full(self):78 return (self.max_items is not None) and (self.num_items >= self.max_items)79 def append(self, x):80 x = np.asarray(x, dtype=np.float32)81 assert x.ndim == 282 if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items):83 if self.num_items >= self.max_items:84 return85 x = x[:self.max_items - self.num_items]86 self.set_num_features(x.shape[1])87 self.num_items += x.shape[0]88 if self.capture_all:89 self.all_features.append(x)90 if self.capture_mean_cov:91 x64 = x.astype(np.float64)92 self.raw_mean += x64.sum(axis=0)93 self.raw_cov += x64.T @ x6494 def append_torch(self, x, num_gpus=1, rank=0):95 assert isinstance(x, torch.Tensor) and x.ndim == 296 assert 0 <= rank < num_gpus97 if num_gpus > 1:98 ys = []99 for src in range(num_gpus):100 y = x.clone()101 torch.distributed.broadcast(y, src=src)102 ys.append(y)103 x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples104 self.append(x.cpu().numpy())105 def get_all(self):106 assert self.capture_all107 return np.concatenate(self.all_features, axis=0)108 def get_all_torch(self):109 return torch.from_numpy(self.get_all())110 def get_mean_cov(self):111 assert self.capture_mean_cov112 mean = self.raw_mean / self.num_items113 cov = self.raw_cov / self.num_items114 cov = cov - np.outer(mean, mean)115 return mean, cov116 def save(self, pkl_file):117 with open(pkl_file, 'wb') as f:118 pickle.dump(self.__dict__, f)119 @staticmethod120 def load(pkl_file):121 with open(pkl_file, 'rb') as f:122 s = dnnlib.EasyDict(pickle.load(f))123 obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items)124 obj.__dict__.update(s)125 return obj126#----------------------------------------------------------------------------127class ProgressMonitor:128 def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000):129 self.tag = tag130 self.num_items = num_items131 self.verbose = verbose132 self.flush_interval = flush_interval133 self.progress_fn = progress_fn134 self.pfn_lo = pfn_lo135 self.pfn_hi = pfn_hi136 self.pfn_total = pfn_total137 self.start_time = time.time()138 self.batch_time = self.start_time139 self.batch_items = 0140 if self.progress_fn is not None:141 self.progress_fn(self.pfn_lo, self.pfn_total)142 def update(self, cur_items):143 assert (self.num_items is None) or (cur_items <= self.num_items)144 if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items):145 return146 cur_time = time.time()147 total_time = cur_time - self.start_time148 time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1)149 if (self.verbose) and (self.tag is not None):150 print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}')151 self.batch_time = cur_time152 self.batch_items = cur_items153 if (self.progress_fn is not None) and (self.num_items is not None):154 self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total)155 def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1):156 return ProgressMonitor(157 tag = tag,158 num_items = num_items,159 flush_interval = flush_interval,160 verbose = self.verbose,161 progress_fn = self.progress_fn,162 pfn_lo = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo,163 pfn_hi = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi,164 pfn_total = self.pfn_total,165 )166#----------------------------------------------------------------------------167def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, **stats_kwargs):168 dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)169 if data_loader_kwargs is None:170 data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)171 # Try to lookup from cache.172 cache_file = None173 if opts.cache:174 # Choose cache file name.175 args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs)176 md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))177 cache_tag = f'{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}'178 cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl')179 # Check if the file exists (all processes must agree).180 flag = os.path.isfile(cache_file) if opts.rank == 0 else False181 if opts.num_gpus > 1:182 flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device)183 torch.distributed.broadcast(tensor=flag, src=0)184 flag = (float(flag.cpu()) != 0)185 # Load.186 if flag:187 return FeatureStats.load(cache_file)188 # Initialize.189 num_items = len(dataset)190 if max_items is not None:191 num_items = min(num_items, max_items)192 stats = FeatureStats(max_items=num_items, **stats_kwargs)193 progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi)194 detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)195 # Main loop.196 item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)]197 for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs):198 if images.shape[1] == 1:199 images = images.repeat([1, 3, 1, 1])200 features = detector(images.to(opts.device), **detector_kwargs)201 stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)202 progress.update(stats.num_items)203 # Save to cache.204 if cache_file is not None and opts.rank == 0:205 os.makedirs(os.path.dirname(cache_file), exist_ok=True)206 temp_file = cache_file + '.' + uuid.uuid4().hex207 stats.save(temp_file)208 os.replace(temp_file, cache_file) # atomic209 return stats210#----------------------------------------------------------------------------211def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, **stats_kwargs):212 if batch_gen is None:213 batch_gen = min(batch_size, 4)214 assert batch_size % batch_gen == 0215 # Setup generator and labels.216 G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device)217 c_iter = iterate_random_labels(opts=opts, batch_size=batch_gen)218 # Initialize.219 stats = FeatureStats(**stats_kwargs)220 assert stats.max_items is not None221 progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi)222 detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)223 # Main loop.224 while not stats.is_full():225 images = []226 for _i in range(batch_size // batch_gen):227 z = torch.randn([batch_gen, G.z_dim], device=opts.device)228 img = G(z=z, c=next(c_iter), **opts.G_kwargs)229 img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)230 images.append(img)231 images = torch.cat(images)232 if images.shape[1] == 1:233 images = images.repeat([1, 3, 1, 1])234 features = detector(images, **detector_kwargs)235 stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)236 progress.update(stats.num_items)237 return stats...
preprocessor.py
Source:preprocessor.py
1"""2Ziwei Zhu3Computer Science and Engineering Department, Texas A&M University4zhuziwei@tamu.edu5"""6import numpy as np7import pandas as pd8class ml1m:9 def __init__(self):10 return11 @staticmethod12 def train(n):13 train_df = pd.read_csv('./data/ml-1m/train_%d.csv' % n)14 vali_df = pd.read_csv('./data/ml-1m/vali_%d.csv' % n)15 num_users = np.max(train_df['userId'])16 num_items = np.max(train_df['movieId'])17 train_R = np.zeros((num_users, num_items)) # training rating matrix18 vali_R = np.zeros((num_users, num_items)) # validation rating matrix19 train_mat = train_df.values20 for i in range(len(train_df)):21 user_idx = int(train_mat[i, 0]) - 122 item_idx = int(train_mat[i, 1]) - 123 train_R[user_idx, item_idx] = 124 vali_mat = vali_df.values25 for i in range(len(vali_df)):26 user_idx = int(vali_mat[i, 0]) - 127 item_idx = int(vali_mat[i, 1]) - 128 vali_R[user_idx, item_idx] = 129 return train_R, vali_R30 @staticmethod31 def test():32 test_df = pd.read_csv('./data/ml-1m/test.csv')33 num_users = np.max(test_df['userId'])34 num_items = np.max(test_df['movieId'])35 test_R = np.zeros((num_users, num_items)) # testing rating matrix36 test_mat = test_df.values37 for i in range(len(test_df)):38 user_idx = int(test_mat[i, 0]) - 139 item_idx = int(test_mat[i, 1]) - 140 test_R[user_idx, item_idx] = 141 train_df = pd.read_csv('./data/ml-1m/train.csv')42 num_users = np.max(train_df['userId'])43 num_items = np.max(train_df['movieId'])44 train_R = np.zeros((num_users, num_items)) # testing rating matrix45 train_mat = train_df.values46 for i in range(len(train_df)):47 user_idx = int(train_mat[i, 0]) - 148 item_idx = int(train_mat[i, 1]) - 149 train_R[user_idx, item_idx] = 150 train_R[user_idx, item_idx] = 151 return train_R, test_R52class Pinterest:53 def __init__(self):54 return55 @staticmethod56 def train(n):57 train_df = pd.read_csv('./data/p/train_%d.csv' % n)58 vali_df = pd.read_csv('./data/p/vali_%d.csv' % n)59 num_users = np.max(train_df['userId'])60 num_items = np.max(train_df['movieId'])61 train_R = np.zeros((num_users, num_items)) # training rating matrix62 vali_R = np.zeros((num_users, num_items)) # validation rating matrix63 train_mat = train_df.values64 for i in range(len(train_df)):65 user_idx = int(train_mat[i, 0]) - 166 item_idx = int(train_mat[i, 1]) - 167 train_R[user_idx, item_idx] = 168 vali_mat = vali_df.values69 for i in range(len(vali_df)):70 user_idx = int(vali_mat[i, 0]) - 171 item_idx = int(vali_mat[i, 1]) - 172 vali_R[user_idx, item_idx] = 173 return train_R, vali_R74 @staticmethod75 def test():76 test_df = pd.read_csv('./data/p/test.csv')77 num_users = np.max(test_df['userId'])78 num_items = np.max(test_df['movieId'])79 test_R = np.zeros((num_users, num_items)) # testing rating matrix80 test_mat = test_df.values81 for i in range(len(test_df)):82 user_idx = int(test_mat[i, 0]) - 183 item_idx = int(test_mat[i, 1]) - 184 test_R[user_idx, item_idx] = 185 train_df = pd.read_csv('./data/p/train.csv')86 num_users = np.max(train_df['userId'])87 num_items = np.max(train_df['movieId'])88 train_R = np.zeros((num_users, num_items)) # testing rating matrix89 train_mat = train_df.values90 for i in range(len(train_df)):91 user_idx = int(train_mat[i, 0]) - 192 item_idx = int(train_mat[i, 1]) - 193 train_R[user_idx, item_idx] = 194 train_R[user_idx, item_idx] = 195 return train_R, test_R96class yelp:97 def __init__(self):98 return99 @staticmethod100 def train(n):101 train_df = pd.read_csv('./data/yelp/train_%d.csv' % n)102 vali_df = pd.read_csv('./data/yelp/vali_%d.csv' % n)103 num_users = np.max(train_df['userId'])104 num_items = np.max(train_df['itemId'])105 train_R = np.zeros((num_users, num_items)) # training rating matrix106 vali_R = np.zeros((num_users, num_items)) # validation rating matrix107 train_mat = train_df.values108 for i in range(len(train_df)):109 user_idx = int(train_mat[i, 0]) - 1110 item_idx = int(train_mat[i, 1]) - 1111 train_R[user_idx, item_idx] = 1112 vali_mat = vali_df.values113 for i in range(len(vali_df)):114 user_idx = int(vali_mat[i, 0]) - 1115 item_idx = int(vali_mat[i, 1]) - 1116 vali_R[user_idx, item_idx] = 1117 return train_R, vali_R118 @staticmethod119 def test():120 test_df = pd.read_csv('./data/yelp/test.csv')121 num_users = np.max(test_df['userId'])122 num_items = np.max(test_df['itemId'])123 test_R = np.zeros((num_users, num_items)) # testing rating matrix124 test_mat = test_df.values125 for i in range(len(test_df)):126 user_idx = int(test_mat[i, 0]) - 1127 item_idx = int(test_mat[i, 1]) - 1128 test_R[user_idx, item_idx] = 1129 train_df = pd.read_csv('./data/yelp/train.csv')130 num_users = np.max(train_df['userId'])131 num_items = np.max(train_df['itemId'])132 train_R = np.zeros((num_users, num_items)) # testing rating matrix133 train_mat = train_df.values134 for i in range(len(train_df)):135 user_idx = int(train_mat[i, 0]) - 1136 item_idx = int(train_mat[i, 1]) - 1137 train_R[user_idx, item_idx] = 1138 train_R[user_idx, item_idx] = 1...
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